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Expert Panel on Thoracic Imaging, Batra K, Walker CM, Little BP, Bang TJ, Bartel TB, Brixey AG, Christensen JD, Cox CW, Hanak M, Khurana S, Madan R, Merchant N, Moore WH, Pandya S, Sanchez LD, Shroff GS, Zagurovskaya M, Chung JH. ACR Appropriateness Criteria® Acute Respiratory Illness in Immunocompetent Patients: 2024 Update. J Am Coll Radiol 2025; 22:S14-S35. [PMID: 40409874 DOI: 10.1016/j.jacr.2025.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2025] [Accepted: 02/24/2025] [Indexed: 05/25/2025]
Abstract
Acute respiratory illness is one of the leading causes of morbidity and mortality amongst infectious diseases worldwide and a major public health issue. Even though most cases are due to self-limited viral infections, a significant number of cases are due to more serious respiratory infections where delay in diagnosis can lead to morbidity and mortality. Imaging plays a key role in the initial diagnosis and management of acute respiratory illness. This document reviews the current literature concerning the appropriate role of imaging in the diagnosis and management of the immunocompetent adult patient initially presenting with acute respiratory illness. Imaging recommendations for adults presenting with asthma or chronic obstructive pulmonary disease exacerbations are discussed. Finally, guidelines for follow-up imaging in suspected pneumonia cases to ensure occult malignancy is not overlooked. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or intermediate, experts may be the primary evidentiary source available to formulate a recommendation.
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Affiliation(s)
| | - Kiran Batra
- UT Southwestern Medical Center, Dallas, Texas.
| | | | - Brent P Little
- Panel Vice-Chair, Mayo Clinic Florida, Jacksonville, Florida
| | | | - Twyla B Bartel
- Global Advanced Imaging, PLLC, Little Rock, Arkansas; Commission on Nuclear Medicine and Molecular Imaging
| | - Anupama G Brixey
- Portland VA Healthcare System and Oregon Health & Science University, Portland, Oregon
| | | | | | - Michael Hanak
- Rush University Medical Center, Chicago, Illinois; American Academy of Family Physicians
| | - Sandhya Khurana
- University of Rochester Medical Center, Rochester, New York; American College of Chest Physicians
| | - Rachna Madan
- Brigham and Women's Hospital, Boston, Massachusetts
| | - Naseema Merchant
- Yale University School of Medicine, New Haven, Connecticut; Society of General Internal Medicine
| | - William H Moore
- New York University Langone Medical Center, New York, New York
| | - Sahil Pandya
- Pulmonologist, University of Kansas Medical Center, Kansas City, Kansas
| | - Leon D Sanchez
- Brigham and Women's Faulkner Hospital, Boston, Massachusetts; American College of Emergency Physicians
| | - Girish S Shroff
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Marianna Zagurovskaya
- Indiana University School of Medicine, Indiana University Health Partners, Indianapolis, Indiana; Committee on Emergency Radiology-GSER
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Straub J, Estrada Lobato E, Paez D, Langs G, Prosch H. Artificial intelligence in respiratory pandemics-ready for disease X? A scoping review. Eur Radiol 2025; 35:1583-1593. [PMID: 39570367 PMCID: PMC11835992 DOI: 10.1007/s00330-024-11183-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 08/02/2024] [Accepted: 09/26/2024] [Indexed: 11/22/2024]
Abstract
OBJECTIVES This study aims to identify repeated previous shortcomings in medical imaging data collection, curation, and AI-based analysis during the early phase of respiratory pandemics. Based on the results, it seeks to highlight essential steps for improving future pandemic preparedness. MATERIALS AND METHODS We searched PubMed/MEDLINE, Scopus, and Cochrane Reviews for articles published from January 1, 2000, to December 31, 2021, using the terms "imaging" or "radiology" or "radiography" or "CT" or "x-ray" combined with "SARS," "MERS," "H1N1," or "COVID-19." WHO and CDC Databases were searched for case definitions. RESULTS Over the last 20 years, the world faced several international health emergencies caused by respiratory diseases such as SARS, MERS, H1N1, and COVID-19. During the same period, major technological advances enabled the analysis of vast amounts of imaging data and the continual development of artificial intelligence algorithms to support radiological diagnosis and prognosis. Timely availability of data proved critical, but so far, data collection attempts were initialized only as individual responses to each outbreak, leading to long delays and hampering unified guidelines and data-driven technology to support the management of pandemic outbreaks. Our findings highlight the multifaceted role of imaging in the early stages of SARS, MERS, H1N1, and COVID-19, and outline possible actions for advancing future pandemic preparedness. CONCLUSIONS Advancing international cooperation and action on these topics is essential to create a functional, effective, and rapid counteraction system to future respiratory pandemics exploiting state of the art imaging and artificial intelligence. KEY POINTS Question What has been the role of radiological data for diagnosis and prognosis in early respiratory pandemics and what challenges were present? Findings International cooperation is essential to developing an effective rapid response system for future respiratory pandemics using advanced imaging and artificial intelligence. Clinical relevance Strengthening global collaboration and leveraging cutting-edge imaging and artificial intelligence are crucial for developing rapid and effective response systems. This approach is essential for improving patient outcomes and managing future respiratory pandemics more effectively.
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Affiliation(s)
- Jennifer Straub
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, 1090, Vienna, Austria
| | - Enrique Estrada Lobato
- Nuclear Medicine and Diagnostic Imaging Section, Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency (IAEA), 1220, Vienna, Austria
| | - Diana Paez
- Nuclear Medicine and Diagnostic Imaging Section, Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency (IAEA), 1220, Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, 1090, Vienna, Austria.
- Christian Doppler Laboratory for Machine Learning Driven Precision Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, 1090, Vienna, Austria.
| | - Helmut Prosch
- Christian Doppler Laboratory for Machine Learning Driven Precision Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, 1090, Vienna, Austria
- Division of General and Paediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, 1090, Vienna, Austria
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Zhou C, Liu C, Liao Z, Pang Y, Sun W. AI for biofabrication. Biofabrication 2024; 17:012004. [PMID: 39433065 DOI: 10.1088/1758-5090/ad8966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Accepted: 10/21/2024] [Indexed: 10/23/2024]
Abstract
Biofabrication is an advanced technology that holds great promise for constructing highly biomimeticin vitrothree-dimensional human organs. Such technology would help address the issues of immune rejection and organ donor shortage in organ transplantation, aiding doctors in formulating personalized treatments for clinical patients and replacing animal experiments. Biofabrication typically involves the interdisciplinary application of biology, materials science, mechanical engineering, and medicine to generate large amounts of data and correlations that require processing and analysis. Artificial intelligence (AI), with its excellent capabilities in big data processing and analysis, can play a crucial role in handling and processing interdisciplinary data and relationships and in better integrating and applying them in biofabrication. In recent years, the development of the semiconductor and integrated circuit industries has propelled the rapid advancement of computer processing power. An AI program can learn and iterate multiple times within a short period, thereby gaining strong automation capabilities for a specific research content or issue. To date, numerous AI programs have been applied to various processes around biofabrication, such as extracting biological information, designing and optimizing structures, intelligent cell sorting, optimizing biomaterials and processes, real-time monitoring and evaluation of models, accelerating the transformation and development of these technologies, and even changing traditional research patterns. This article reviews and summarizes the significant changes and advancements brought about by AI in biofabrication, and discusses its future application value and direction.
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Affiliation(s)
- Chang Zhou
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, People's Republic of China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing 100084, People's Republic of China
| | - Changru Liu
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, People's Republic of China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing 100084, People's Republic of China
| | - Zhendong Liao
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, People's Republic of China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing 100084, People's Republic of China
| | - Yuan Pang
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, People's Republic of China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing 100084, People's Republic of China
| | - Wei Sun
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, People's Republic of China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing 100084, People's Republic of China
- Department of Mechanical Engineering, Drexel University, Philadelphia, PA 19104, United States of America
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Honein-AbouHaidar G, Rizkallah C, Bou Akl I, Morgano GP, Vrbová T, van Deventer E, Del Rosario Perez M, Akl EA. Understanding contextual and practical factors to inform WHO recommendations on using chest imaging to monitor COVID-19 pulmonary sequelae: a qualitative study exploring stakeholders' perspective. Health Res Policy Syst 2024; 22:67. [PMID: 38862978 PMCID: PMC11167887 DOI: 10.1186/s12961-023-01088-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 12/02/2023] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND A recommendation by the World Health Organization (WHO) was issued about the use of chest imaging to monitor pulmonary sequelae following recovery from COVID-19. This qualitative study aimed to explore the perspective of key stakeholders to understand their valuation of the outcome of the proposition, preferences for the modalities of chest imaging, acceptability, feasibility, impact on equity and practical considerations influencing the implementation of using chest imaging. METHODS A qualitative descriptive design using in-depth interviews approach. Key stakeholders included adult patients who recovered from the acute illness of COVID-19, and providers caring for those patients. The Evidence to Decision (EtD) conceptual framework was used to guide data collection of contextual and practical factors related to monitoring using imaging. Data analysis was based on the framework thematic analysis approach. RESULTS 33 respondents, including providers and patients, were recruited from 15 different countries. Participants highly valued the ability to monitor progression and resolution of long-term sequelae but recommended the avoidance of overuse of imaging. Their preferences for the imaging modalities were recorded along with pros and cons. Equity concerns were reported across countries (e.g., access to resources) and within countries (e.g., disadvantaged groups lacked access to insurance). Both providers and patients accepted the use of imaging, some patients were concerned about affordability of the test. Facilitators included post- recovery units and protocols. Barriers to feasibility included low number of specialists in some countries, access to imaging tests among elderly living in nursing homes, experience of poor coordination of care, emotional exhaustion, and transportation challenges driving to a monitoring site. CONCLUSION We were able to demonstrate that there is a high value and acceptability using imaging but there were factors influencing feasibility, equity and some practical considerations associated with implementation. We had a few suggestions to be considered by the expert panel in the formulation of the guideline to facilitate its implementation such as using validated risk score predictive tools for lung complications to recommend the appropriate imaging modality and complementary pulmonary function test.
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Affiliation(s)
| | - Cynthia Rizkallah
- Hariri School of Nursing, American University of Beirut, Beirut, Lebanon
| | - Imad Bou Akl
- Department of Internal Medicine, American University of Beirut, Beirut, Lebanon
| | - Gian Paolo Morgano
- Department of Health Research Methods, Evidence and Impact McMaster University, 1280 Main Street West, Hamilton, Canada
| | - Tereza Vrbová
- Czech National Centre for Evidence-Based Healthcare and Knowledge Translation (CEBHC-KT), Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Emilie van Deventer
- Radiation and Health Unit, Department of Environment, Climate Change and Health, World Health Organization, Geneva, Switzerland.
| | - Maria Del Rosario Perez
- Radiation and Health Unit, Department of Environment, Climate Change and Health, World Health Organization, Geneva, Switzerland
| | - Elie A Akl
- Department of Medicine, American University of Beirut, Riad-El-Solh, P.O. Box 11-0236, Beirut, 1107 2020, Lebanon.
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Tenda ED, Yunus RE, Zulkarnaen B, Yugo MR, Pitoyo CW, Asaf MM, Islamiyati TN, Pujitresnani A, Setiadharma A, Henrina J, Rumende CM, Wulani V, Harimurti K, Lydia A, Shatri H, Soewondo P, Yusuf PA. Comparison of the Discrimination Performance of AI Scoring and the Brixia Score in Predicting COVID-19 Severity on Chest X-Ray Imaging: Diagnostic Accuracy Study. JMIR Form Res 2024; 8:e46817. [PMID: 38451633 PMCID: PMC10958333 DOI: 10.2196/46817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 09/28/2023] [Accepted: 12/29/2023] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND The artificial intelligence (AI) analysis of chest x-rays can increase the precision of binary COVID-19 diagnosis. However, it is unknown if AI-based chest x-rays can predict who will develop severe COVID-19, especially in low- and middle-income countries. OBJECTIVE The study aims to compare the performance of human radiologist Brixia scores versus 2 AI scoring systems in predicting the severity of COVID-19 pneumonia. METHODS We performed a cross-sectional study of 300 patients suspected with and with confirmed COVID-19 infection in Jakarta, Indonesia. A total of 2 AI scores were generated using CAD4COVID x-ray software. RESULTS The AI probability score had slightly lower discrimination (area under the curve [AUC] 0.787, 95% CI 0.722-0.852). The AI score for the affected lung area (AUC 0.857, 95% CI 0.809-0.905) was almost as good as the human Brixia score (AUC 0.863, 95% CI 0.818-0.908). CONCLUSIONS The AI score for the affected lung area and the human radiologist Brixia score had similar and good discrimination performance in predicting COVID-19 severity. Our study demonstrated that using AI-based diagnostic tools is possible, even in low-resource settings. However, before it is widely adopted in daily practice, more studies with a larger scale and that are prospective in nature are needed to confirm our findings.
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Affiliation(s)
- Eric Daniel Tenda
- Department of Internal Medicine, Pulmonology and Critical Care Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Reyhan Eddy Yunus
- Department of Radiology, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Benny Zulkarnaen
- Department of Radiology, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Muhammad Reynalzi Yugo
- Department of Radiology, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Ceva Wicaksono Pitoyo
- Department of Internal Medicine, Pulmonology and Critical Care Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Moses Mazmur Asaf
- Department of Radiology, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Tiara Nur Islamiyati
- Department of Internal Medicine, Pulmonology and Critical Care Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Arierta Pujitresnani
- Department of Medical Physiology and Biophysics/ Medical Technology Cluster IMERI, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Andry Setiadharma
- Department of Internal Medicine, Pulmonology and Critical Care Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Joshua Henrina
- Department of Internal Medicine, Pulmonology and Critical Care Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Cleopas Martin Rumende
- Department of Internal Medicine, Pulmonology and Critical Care Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Vally Wulani
- Department of Radiology, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Kuntjoro Harimurti
- Department of Internal Medicine, Geriatric Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Aida Lydia
- Department of Internal Medicine, Nephrology and Hypertension Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Hamzah Shatri
- Department of Internal Medicine, Psychosomatic Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Pradana Soewondo
- Department of Internal Medicine, Endocrinology - Metabolism - Diabetes division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia
| | - Prasandhya Astagiri Yusuf
- Department of Medical Physiology and Biophysics/ Medical Technology Cluster IMERI, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
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Jiang RM, Xie ZD, Jiang Y, Lu XX, Jin RM, Zheng YJ, Shang YX, Xu BP, Liu ZS, Lu G, Deng JK, Liu GH, Wang XC, Wang JS, Feng LZ, Liu W, Zheng Y, Shu SN, Lu M, Luo WJ, Liu M, Cui YX, Ye LP, Shen AD, Liu G, Gao LW, Xiong LJ, Bai Y, Lin LK, Wei Z, Xue FX, Wang TY, Zhao DC, Shao JB, Ng DKK, Wong GWK, Zhao ZY, Li XW, Yang YH, Shen KL. Diagnosis, treatment and prevention of severe acute respiratory syndrome coronavirus 2 infection in children: experts' consensus statement updated for the Omicron variant. World J Pediatr 2024; 20:272-286. [PMID: 37676610 DOI: 10.1007/s12519-023-00745-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 06/29/2023] [Indexed: 09/08/2023]
Affiliation(s)
- Rong-Meng Jiang
- Diagnosis and Treatment Center of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China
| | - Zheng-De Xie
- Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, Research Unit of Critical Infection in Children, Chinese Academy of Medical Sciences, 2019RU016, Laboratory of Infection and Virology, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Yi Jiang
- Department of Pediatrics, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Xiao-Xia Lu
- Department of Respiratory, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430016, China
| | - Run-Ming Jin
- Department of Pediatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Yue-Jie Zheng
- Department of Respiratory, Shenzhen Children's Hospital, Shenzhen, 518038, China
| | - Yun-Xiao Shang
- Department of Pediatric Respiratory, Shengjing Hospital Affiliated to China Medical University, Shenyang, 110004, China
| | - Bao-Ping Xu
- Department of Respiratory, Beijing Children's Hospital, Capital Medical University, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, Beijing, 100045, China
| | - Zhi-Sheng Liu
- Department of Neurology, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430016, China
| | - Gen Lu
- Department of Respiratory, Guangzhou Women and Children's Medical Center, Guangzhou, 510623, China
| | - Ji-Kui Deng
- Department of Infectious Diseases, Shenzhen Children's Hospital, Shenzhen, 518038, China
| | - Guang-Hua Liu
- Department of Pediatrics, Fujian Branch of Shanghai Children's Medical Center, Fujian Children's Hospital, Fuzhou, 350005, China
| | - Xiao-Chuan Wang
- Department of Clinical Immunology and Allergy, Children's Hospital of Fudan University, National Center for Children's Health, Shanghai, 201102, China
| | - Jian-She Wang
- Department of Infectious Diseases, Children's Hospital of Fudan University, National Center for Children's Health, Shanghai, 201102, China
| | - Lu-Zhao Feng
- School of Population Medicine and Public Health, Chinese Academy of Medical Science, Peking Union Medical College, Beijing, 100730, China
| | - Wei Liu
- Children's Hospital of Tianjin University, Tianjin Children's Hospital, Tianjin, 300134, China
| | - Yi Zheng
- Beijing Key Laboratory of Diagnosis and Treatment of Mental Disorders, National Clinical Research Center for Mental and Psychological Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Sai-Nan Shu
- Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Min Lu
- Department of Respiratory, Shanghai Children's Hospital, Shanghai, 200062, China
| | - Wan-Jun Luo
- Office of Infection Management, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430016, China
| | - Miao Liu
- Department of Pediatrics, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Yu-Xia Cui
- Department of Pediatrics, Guizhou Provincial People's Hospital, Guiyang, 550002, China
| | - Le-Ping Ye
- Department of Pediatrics, Peking University First Hospital, Beijing, 100034, China
| | - A-Dong Shen
- Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, Beijing, 100045, China
| | - Gang Liu
- Department of Infectious Diseases, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Li-Wei Gao
- Department of Respiratory, Beijing Children's Hospital, Capital Medical University, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, Beijing, 100045, China
| | - Li-Juan Xiong
- Department of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Yan Bai
- Department of Pediatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Li-Kai Lin
- Hospital Management Institute of Wuhan University, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Zhuang Wei
- Children's Health Care Center, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, 100045, China
| | - Feng-Xia Xue
- Department of Respiratory, Beijing Children's Hospital, Capital Medical University, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, Beijing, 100045, China
| | - Tian-You Wang
- Hematology and Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Dong-Chi Zhao
- Department of Pediatrics, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Jian-Bo Shao
- Department of Radiology, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430016, China
| | - Daniel Kwok-Keung Ng
- Department of Pediatrics, Hong Kong Sanatorium & Hospital, Hong Kong, 999077, China
| | - Gary Wing-Kin Wong
- Department of Pediatrics, Prince of Wales Hospital, Chinese University of Hong Kong, Hong Kong, 999077, China
| | - Zheng-Yan Zhao
- Department of Developmental Behavior, Children's Hospital, Zhejiang University College of Medicine, Hangzhou, 310051, China.
| | - Xing-Wang Li
- Diagnosis and Treatment Center of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China.
| | - Yong-Hong Yang
- Department of Respiratory, Shenzhen Children's Hospital, Shenzhen, 518038, China.
- Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, Beijing, 100045, China.
| | - Kun-Ling Shen
- Department of Respiratory, Shenzhen Children's Hospital, Shenzhen, 518038, China.
- Department of Respiratory, Beijing Children's Hospital, Capital Medical University, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, Beijing, 100045, China.
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7
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Steuwe A, Kamp B, Afat S, Akinina A, Aludin S, Bas EG, Berger J, Bohrer E, Brose A, Büttner SM, Ehrengut C, Gerwing M, Grosu S, Gussew A, Güttler F, Heinrich A, Jiraskova P, Kloth C, Kottlors J, Kuennemann MD, Liska C, Lubina N, Manzke M, Meinel FG, Meyer HJ, Mittermeier A, Persigehl T, Schmill LP, Steinhardt M, The Racoon Study Group, Antoch G, Valentin B. Standardization of a CT Protocol for Imaging Patients with Suspected COVID-19-A RACOON Project. Bioengineering (Basel) 2024; 11:207. [PMID: 38534481 DOI: 10.3390/bioengineering11030207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/09/2024] [Accepted: 02/15/2024] [Indexed: 03/28/2024] Open
Abstract
CT protocols that diagnose COVID-19 vary in regard to the associated radiation exposure and the desired image quality (IQ). This study aims to evaluate CT protocols of hospitals participating in the RACOON (Radiological Cooperative Network) project, consolidating CT protocols to provide recommendations and strategies for future pandemics. In this retrospective study, CT acquisitions of COVID-19 patients scanned between March 2020 and October 2020 (RACOON phase 1) were included, and all non-contrast protocols were evaluated. For this purpose, CT protocol parameters, IQ ratings, radiation exposure (CTDIvol), and central patient diameters were sampled. Eventually, the data from 14 sites and 534 CT acquisitions were analyzed. IQ was rated good for 81% of the evaluated examinations. Motion, beam-hardening artefacts, or image noise were reasons for a suboptimal IQ. The tube potential ranged between 80 and 140 kVp, with the majority between 100 and 120 kVp. CTDIvol was 3.7 ± 3.4 mGy. Most healthcare facilities included did not have a specific non-contrast CT protocol. Furthermore, CT protocols for chest imaging varied in their settings and radiation exposure. In future, it will be necessary to make recommendations regarding the required IQ and protocol parameters for the majority of CT scanners to enable comparable IQ as well as radiation exposure for different sites but identical diagnostic questions.
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Affiliation(s)
- Andrea Steuwe
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Benedikt Kamp
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Alena Akinina
- Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), 06120 Halle, Germany
| | - Schekeb Aludin
- Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein Campus Kiel, 24105 Kiel, Germany
| | - Elif Gülsah Bas
- Department of Diagnostic and Interventional Radiology, University Hospital of Marburg, 35043 Marburg, Germany
| | - Josephine Berger
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Evelyn Bohrer
- Department of Diagnostic and Interventional Radiology, University Hospital Giessen, Justus Liebig University, Klinikstr. 33, 35392 Giessen, Germany
| | - Alexander Brose
- Department of Diagnostic and Interventional Radiology, University Hospital Giessen, Justus Liebig University, Klinikstr. 33, 35392 Giessen, Germany
| | - Susanne Martina Büttner
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Constantin Ehrengut
- Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Liebigstraße 20, 04103 Leipzig, Germany
| | - Mirjam Gerwing
- Clinic of Radiology, University of Münster, 48149 Münster, Germany
| | - Sergio Grosu
- Department of Radiology, LMU University Hospital, LMU Munich, 81377 Munich, Germany
| | - Alexander Gussew
- Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), 06120 Halle, Germany
| | - Felix Güttler
- Department of Radiology, Jena University Hospital, Friedrich Schiller University, 07747 Jena, Germany
| | - Andreas Heinrich
- Department of Radiology, Jena University Hospital, Friedrich Schiller University, 07747 Jena, Germany
| | - Petra Jiraskova
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany
| | - Christopher Kloth
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | | | - Christian Liska
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Nora Lubina
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Mathias Manzke
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Schillingallee 36, 18057 Rostock, Germany
| | - Felix G Meinel
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Schillingallee 36, 18057 Rostock, Germany
| | - Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Liebigstraße 20, 04103 Leipzig, Germany
| | - Andreas Mittermeier
- Department of Radiology, LMU University Hospital, LMU Munich, 81377 Munich, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Lars-Patrick Schmill
- Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein Campus Kiel, 24105 Kiel, Germany
| | - Manuel Steinhardt
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany
| | | | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Birte Valentin
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
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Chua MT, Boon Y, Yeoh CK, Li Z, Goh CJM, Kuan WS. Point-of-care ultrasound use in COVID-19: a narrative review. ANNALS OF TRANSLATIONAL MEDICINE 2024; 12:13. [PMID: 38304913 PMCID: PMC10777239 DOI: 10.21037/atm-23-1403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 06/25/2023] [Indexed: 02/03/2024]
Abstract
Background and Objective The coronavirus disease 2019 (COVID-19) pandemic that began in early 2020 resulted in significant mortality from respiratory tract infections. Existing imaging modalities such as chest X-ray (CXR) lacks sensitivity in its diagnosis while computed tomography (CT) scan carries risks of radiation and contamination. Point-of-care ultrasound (POCUS) has the advantage of bedside testing with higher diagnostic accuracy. We aim to describe the various applications of POCUS for patients with suspected severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in the emergency department (ED) and intensive care unit (ICU). Methods We performed literature search on the use of POCUS in the diagnosis and management of COVID-19 in MEDLINE, Embase and Scopus databases using the following search terms: "ultrasonography", "ultrasound", "COVID-19", "SARS-CoV-2", "SARS-CoV-2 variants", "emergency services", "emergency department" and "intensive care units". Search was performed independently by two reviewers with any discrepancy adjudicated by a third member. Key Content and Findings Lung POCUS in patients with COVID-19 shows different ultrasonographic features from pulmonary oedema, bacterial pneumonia, and other viral pneumonia, thus useful in differentiating between these conditions. It is more sensitive than CXR, and more accessible and widely available than CT scan. POCUS can be used to diagnose COVID-19 pneumonia, screen for COVID-19-related pulmonary and extrapulmonary complications, and guide management of ICU patients, such as timing of ventilator weaning based on lung POCUS findings. Conclusions POCUS is a useful and rapid point-of-care modality that can be used to aid in diagnosis, management, and risk stratification of COVID-19 patients in different healthcare settings.
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Affiliation(s)
- Mui Teng Chua
- Emergency Medicine Department, National University Hospital, National University Health System, Singapore, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yuru Boon
- Emergency Medicine Department, National University Hospital, National University Health System, Singapore, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Chew Kiat Yeoh
- Emergency Medicine Department, National University Hospital, National University Health System, Singapore, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zisheng Li
- Emergency Medicine Department, National University Hospital, National University Health System, Singapore, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Carmen Jia Man Goh
- Emergency Department, Ng Teng Fong General Hospital, Singapore, Singapore
| | - Win Sen Kuan
- Emergency Medicine Department, National University Hospital, National University Health System, Singapore, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Benchoufi M, Bokobza J, Chauvin A, Dion E, Baranne ML, Levan F, Gautier M, Cantin D, d'Humières T, Gil-Jardiné C, Benenati S, Orbelin M, Martinez M, Pierre-Kahn N, Diallo A, Vicaut E, Bourrier P. Comparison Between Lung Ultrasonography Score in the Emergency Department and Clinical Outcomes of Patients With or With Suspected COVID-19: An Observational Multicentric Study. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:2883-2895. [PMID: 37688781 DOI: 10.1002/jum.16329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 07/24/2023] [Accepted: 08/23/2023] [Indexed: 09/11/2023]
Abstract
OBJECTIVE Chest CT is the reference test for assessing pulmonary injury in suspected or diagnosed COVID-19 with signs of clinical severity. This study aimed to evaluate the association of a lung ultrasonography score and unfavorable clinical evolution at 28 days. METHODS The eChoVid is a multicentric study based on routinely collected data that was conducted in 8 emergency units in France; patients were included between March 19, 2020 and April 28, 2020 and underwent lung ultrasonography, a short clinical assessment by 2 emergency physicians blinded to each other's assessment, and chest CT. Lung ultrasonography consisted of scoring lesions from 0 to 3 in 8 chest zones, thus defining a global score (GS) of severity from 0 to 24. The primary outcome was the association of lung damage severity as assessed by the GS at day 0 and patient status at 28 days. Secondary outcomes were comparing the performance between GS and CT scan and the performance between a new trainee physician and an ultrasonography expert in scores. RESULTS For the 328 patients analyzed, the GS showed good performance in predicting clinical worsening at 28 days (area under the receiver operating characteristic curve [AUC] 0.83, sensitivity 84.2%, specificity 76.4%). The GS showed good performance in predicting the CT severity assessment (AUC 0.84, sensitivity 77.2%, specificity 83.7%). CONCLUSION A lung ultrasonography GS is a simple tool that can be used in the emergency department to predict unfavorable assessment at 28 days in patients with COVID-19.
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Affiliation(s)
- Mehdi Benchoufi
- Center for Clinical Epidemiology, Hôtel-Dieu Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
- METHODS Team, Center for Research in Epidemiology and Statistics Sorbonne Paris Cité (CRESS-UMR 1153), Paris, France
- PICUS, Point of Care UltraSound Institute, Paris, France
| | - Jerôme Bokobza
- PICUS, Point of Care UltraSound Institute, Paris, France
- Adult Emergency Department, Hôpital Cochin, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Anthony Chauvin
- Adult Emergency Department, Hôpital Lariboisière, Inserm U942 MASCOT, Université de Paris, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Elisabeth Dion
- Imaging Department Hôtel Dieu, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
- Centre de Recherche de l'Inflammation (CRI), INSERM U1149, Paris, France
| | - Marie-Laure Baranne
- Center for Clinical Epidemiology, Hôtel-Dieu Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
- PICUS, Point of Care UltraSound Institute, Paris, France
| | - Fabien Levan
- Adult Emergency Department, Hôpital Cochin, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Maxime Gautier
- PICUS, Point of Care UltraSound Institute, Paris, France
- Adult Emergency Department, Hôpital Cochin, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Delphine Cantin
- Imaging Department Hôtel Dieu, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Thomas d'Humières
- Physiology Department, Henri Mondor University Hospital, Créteil, France
| | - Cédric Gil-Jardiné
- Adult Emergency Department SAMU-SMUR, Pellegrin Hospital, University Hospital Center, Bordeaux, France
- Bordeaux Population Health, INSERM U1219, IETO Team, Bordeaux University, Bordeaux, France
| | - Sylvain Benenati
- Adult Emergency Department, Hospital Group South Ile-de-France, Melun, France
| | - Mathieu Orbelin
- Adult Emergency Department, New Civil Hospital, Strasbourg, France
| | - Mikaël Martinez
- Adult Emergency Department, Forez Hospital Center, Montbrison, France
- Nord Emergency Network Ligérien Ardèche (REULIAN), Hospital Center Le Corbusier, Firminy, France
| | - Nathalie Pierre-Kahn
- Imaging Department Hôtel Dieu, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Abdourahmane Diallo
- Clinical Trial Unit Hospital, Lariboisière St-Louis AP-HP, Paris University, Paris, France
| | - Eric Vicaut
- Clinical Trial Unit Hospital, Lariboisière St-Louis AP-HP, Paris University, Paris, France
| | - Pierre Bourrier
- Imaging Department Saint-Louis Hospital, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France
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10
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Heyne TF, Negishi K, Choi DS, Al Saud AA, Marinacci LX, Smithedajkul PY, Devaraj LR, Little BP, Mendoza DP, Flores EJ, Petranovic M, Toal SP, Shokoohi H, Liteplo AS, Geisler BP. Handheld Lung Ultrasound to Detect COVID-19 Pneumonia in Inpatients: A Prospective Cohort Study. POCUS JOURNAL 2023; 8:175-183. [PMID: 38099168 PMCID: PMC10721309 DOI: 10.24908/pocus.v8i2.16484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Background: Chest imaging, including chest X-ray (CXR) and computed tomography (CT), can be a helpful adjunct to nucleic acid test (NAT) in the diagnosis and management of Coronavirus Disease 2019 (COVID-19). Lung point of care ultrasound (POCUS), particularly with handheld devices, is an imaging alternative that is rapid, highly portable, and more accessible in low-resource settings. A standardized POCUS scanning protocol has been proposed to assess the severity of COVID-19 pneumonia, but it has not been sufficiently validated to assess diagnostic accuracy for COVID-19 pneumonia. Purpose: To assess the diagnostic performance of a standardized lung POCUS protocol using a handheld POCUS device to detect patients with either a positive NAT or a COVID-19-typical pattern on CT scan. Methods: Adult inpatients with confirmed or suspected COVID-19 and a recent CT were recruited from April to July 2020. Twelve lung zones were scanned with a handheld POCUS machine. Images were reviewed independently by blinded experts and scored according to the proposed protocol. Patients were divided into low, intermediate, and high suspicion based on their POCUS score. Results: Of 79 subjects, 26.6% had a positive NAT and 31.6% had a typical CT pattern. The receiver operator curve for POCUS had an area under the curve (AUC) of 0.787 for positive NAT and 0.820 for a typical CT. Using a two-point cutoff system, POCUS had a sensitivity of 0.90 and 1.00 compared to NAT and typical CT pattern, respectively, at the lower cutoff; it had a specificity of 0.90 and 0.89 compared to NAT and typical CT pattern at the higher cutoff, respectively. Conclusions: The proposed lung POCUS protocol with a handheld device showed reasonable diagnostic performance to detect inpatients with a positive NAT or typical CT pattern for COVID-19. Particularly in low-resource settings, POCUS with handheld devices may serve as a helpful adjunct for persons under investigation for COVID-19 pneumonia.
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Affiliation(s)
- Thomas F Heyne
- Department of Medicine, Massachusetts General HospitalBoston, MAUSA
- Department of Pediatrics, Massachusetts General HospitalBoston, MAUSA
| | - Kay Negishi
- Department of Medicine, Massachusetts General HospitalBoston, MAUSA
| | - Daniel S Choi
- Department of Emergency Medicine, Massachusetts General HospitalBoston, MAUSA
| | - Ahad A Al Saud
- Department of Emergency Medicine, Massachusetts General HospitalBoston, MAUSA
- Department of Emergency Medicine, King Saud University College of MedicineRiyadhSaudi Arabia
| | - Lucas X Marinacci
- Richard A. and Susan F. Smith Center for Outcomes Research, Beth Israel Deaconess Medical CenterBoston, MAUSA
| | | | - Lily R Devaraj
- Department of Medicine, Massachusetts General HospitalBoston, MAUSA
- Department of Pediatrics, Massachusetts General HospitalBoston, MAUSA
| | - Brent P Little
- Department of Radiology, Massachusetts General HospitalBoston, MAUSA
| | - Dexter P Mendoza
- Department of Radiology, Massachusetts General HospitalBoston, MAUSA
| | - Efren J Flores
- Department of Radiology, Massachusetts General HospitalBoston, MAUSA
| | - Milena Petranovic
- Department of Radiology, Massachusetts General HospitalBoston, MAUSA
| | - Steven P Toal
- Department of Medicine, Massachusetts General HospitalBoston, MAUSA
| | - Hamid Shokoohi
- Department of Emergency Medicine, Massachusetts General HospitalBoston, MAUSA
| | - Andrew S Liteplo
- Department of Emergency Medicine, Massachusetts General HospitalBoston, MAUSA
| | - Benjamin P Geisler
- Department of Medicine, Massachusetts General HospitalBoston, MAUSA
- Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig Maximilian UniversityMunichGermany
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11
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Yaacoub S, Chamseddine F, Jaber F, Blazic I, Frija G, Akl EA. Exploring the concordance of recommendations across guidelines on chest imaging for the diagnosis and management of COVID-19: A proposed methodological approach based on a case study. PLoS One 2023; 18:e0288359. [PMID: 37498898 PMCID: PMC10374079 DOI: 10.1371/journal.pone.0288359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 06/23/2023] [Indexed: 07/29/2023] Open
Abstract
OBJECTIVE To describe a methodological approach to explore the concordance of recommendations across guidelines and its application to the case of the WHO recommendations on chest imaging for the diagnosis and management of COVID-19. STUDY DESIGN AND SETTING We followed a methodological approach applied to a case study that included: defining the 'reference guideline' (i.e., the WHO guidance) and the 'reference recommendations'; searching for 'related guidelines' and identifying 'related recommendations'; constructing the PICO for the recommendations; assessing the matching of the PICO of each related recommendation to the PICO corresponding reference recommendation; and assessing the concordance between the PICO-matching recommendations. RESULTS We identified a total of 89 related recommendations from 22 related guidelines. Out of the 89 related recommendations, 43 partly matched and 1 entirely matched one of the reference recommendations, and out of these, 8 were concordant with one of the reference recommendations. When considering the seven reference recommendations, they had a median of 12 related recommendations (range 3-17), a median of 7 PICO-matching recommendations (range 0-13), and a median of 1 concordant recommendation (range 0-4). CONCLUSION Following a detailed methodological approach, we were able to explore the concordance between our reference recommendations and related recommendations from other guidelines. A relatively low percentage of recommendations was concordant.
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Affiliation(s)
- Sally Yaacoub
- Clinical Research Institute, American University of Beirut, Beirut, Lebanon
| | | | - Farah Jaber
- Department of Internal Medicine, American University of Beirut, Beirut, Lebanon
| | - Ivana Blazic
- Clinical Hospital Centre Zemun, Belgrade, Serbia
| | | | - Elie A Akl
- Department of Internal Medicine, American University of Beirut, Beirut, Lebanon
- Department of Health Research Methods, Evidence & Impact, McMaster University, Ontario, Canada
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12
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Shin HJ, Kim JY, Hong JH, Lee MS, Yi J, Kwon YS, Lee JY. Assessment of the Suitability of the Fleischner Society Imaging Guidelines in Evaluating Chest Radiographs of COVID-19 Patients. J Korean Med Sci 2023; 38:e199. [PMID: 37401494 DOI: 10.3346/jkms.2023.38.e199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/16/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The Fleischner Society established consensus guidelines for imaging in patients with coronavirus disease 2019 (COVID-19). We investigated the prevalence of pneumonia and the adverse outcomes by dividing groups according to the symptoms and risk factors of patients and assessed the suitability of the Fleischner society imaging guidelines in evaluating chest radiographs of COVID-19 patients. METHODS From February 2020 to May 2020, 685 patients (204 males, mean 58 ± 17.9 years) who were diagnosed with COVID-19 and hospitalized were included. We divided patients into four groups according to the severity of symptoms and presence of risk factors (age > 65 years and presence of comorbidities). The patient groups were defined as follows: group 1 (asymptomatic patients), group 2 (patients with mild symptoms without risk factors), group 3 (patients with mild symptoms and risk factors), and group 4 (patients with moderate to severe symptoms). According to the Fleischner society, chest imaging is not indicated for groups 1-2 but is indicated for groups 3-4. We compared the prevalence and score of pneumonia on chest radiographs and compare the adverse outcomes (progress to severe pneumonia, intensive care unit admission, and death) between groups. RESULTS Among the 685 COVID-19 patients, 138 (20.1%), 396 (57.8%), 102 (14.9%), and 49 (7.1%) patients corresponded to groups 1 to 4, respectively. Patients in groups 3-4 were significantly older and showed significantly higher prevalence rates of pneumonia (group 1-4: 37.7%, 51.3%, 71.6%, and 98%, respectively, P < 0.001) than those in groups 1-2. Adverse outcomes were also higher in groups 3-4 than in groups 1-2 (group 1-4: 8.0%, 3.5%, 6.9%, and 51%, respectively, P < 0.001). Patients with adverse outcomes in group 1 were initially asymptomatic but symptoms developed during follow-up. They were older (mean age, 80 years) and most of them had comorbidities (81.8%). Consistently asymptomatic patients had no adverse events. CONCLUSION The prevalence of pneumonia and adverse outcomes were different according to the symptoms and risk factors in COVID-19 patients. Therefore, as the Fleischner Society recommended, evaluation and monitoring of COVID-19 pneumonia using chest radiographs is necessary for old symptomatic patients with comorbidities.
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Affiliation(s)
- Hyo Ju Shin
- Department of Radiology, Dongsan Hospital, Keimyung University College of Medicine, Daegu, Korea
| | - Jin Young Kim
- Department of Radiology, Dongsan Hospital, Keimyung University College of Medicine, Daegu, Korea.
| | - Jung Hee Hong
- Department of Radiology, Dongsan Hospital, Keimyung University College of Medicine, Daegu, Korea
| | - Mu Sook Lee
- Department of Radiology, Dongsan Hospital, Keimyung University College of Medicine, Daegu, Korea
| | - Jaehyuck Yi
- Department of Radiology, Dongsan Hospital, Keimyung University College of Medicine, Daegu, Korea
| | - Yong Shik Kwon
- Department of Internal Medicine, Dongsan Hospital, Keimyung University College of Medicine, Daegu, Korea
| | - Ji Yeon Lee
- Department of Internal Medicine, Dongsan Hospital, Keimyung University College of Medicine, Daegu, Korea
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13
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Clofent D, Culebras M, Felipe-Montiel A, Arjona-Peris M, Granados G, Sáez M, Pilia F, Ferreiro A, Álvarez A, Loor K, Bosch-Nicolau P, Polverino E. Serial lung ultrasound in monitoring viral pneumonia: the lesson learned from COVID-19. ERJ Open Res 2023; 9:00017-2023. [PMID: 37583967 PMCID: PMC10423983 DOI: 10.1183/23120541.00017-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 05/15/2023] [Indexed: 08/17/2023] Open
Abstract
Background Lung ultrasound (LUS) has proven to be useful in the evaluation of lung involvement in COVID-19. However, its effectiveness for predicting the risk of severe disease is still up for debate. The aim of the study was to establish the prognostic accuracy of serial LUS examinations in the prediction of clinical deterioration in hospitalised patients with COVID-19. Methods Prospective single-centre cohort study of patients hospitalised for COVID-19. The study protocol consisted of a LUS examination within 24 h from admission and a follow-up examination on day 3 of hospitalisation. Lung involvement was evaluated by a 14-area LUS score. The primary end-point was the ability of LUS to predict clinical deterioration defined as need for intensive respiratory support with high-flow oxygen or invasive mechanical ventilation. Results 200 patients were included and 35 (17.5%) of them reached the primary end-point and were transferred to the intensive care unit (ICU). The LUS score at admission had been significantly higher in the ICU group than in the non-ICU group (22 (interquartile range (IQR) 20-26) versus 12 (IQR 8-15)). A LUS score at admission ≥17 was shown to be the best cut-off point to discriminate patients at risk of deterioration (area under the curve (AUC) 0.95). The absence of progression in LUS score on day 3 significantly increased the prediction accuracy by ruling out deterioration with a negative predictive value of 99.29%. Conclusion Serial LUS is a reliable tool in predicting the risk of respiratory deterioration in patients hospitalised due to COVID-19 pneumonia. LUS could be further implemented in the future for risk stratification of viral pneumonia.
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Affiliation(s)
- David Clofent
- Department of Respiratory Medicine, Vall d'Hebron University Hospital, Barcelona, Spain
- Vall d'Hebron Institut de Recerca, Barcelona, Spain
- CIBER Enfermedades Respiratorias, Barcelona, Spain
| | - Mario Culebras
- Department of Respiratory Medicine, Vall d'Hebron University Hospital, Barcelona, Spain
- Vall d'Hebron Institut de Recerca, Barcelona, Spain
| | - Almudena Felipe-Montiel
- Department of Respiratory Medicine, Vall d'Hebron University Hospital, Barcelona, Spain
- Vall d'Hebron Institut de Recerca, Barcelona, Spain
| | - Marta Arjona-Peris
- Department of Respiratory Medicine, Vall d'Hebron University Hospital, Barcelona, Spain
- Vall d'Hebron Institut de Recerca, Barcelona, Spain
| | - Galo Granados
- Department of Respiratory Medicine, Vall d'Hebron University Hospital, Barcelona, Spain
- Vall d'Hebron Institut de Recerca, Barcelona, Spain
| | - María Sáez
- Department of Respiratory Medicine, Vall d'Hebron University Hospital, Barcelona, Spain
- Vall d'Hebron Institut de Recerca, Barcelona, Spain
| | - Florencia Pilia
- Department of Respiratory Medicine, Vall d'Hebron University Hospital, Barcelona, Spain
- Vall d'Hebron Institut de Recerca, Barcelona, Spain
| | - Antía Ferreiro
- Department of Respiratory Medicine, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Antonio Álvarez
- Department of Respiratory Medicine, Vall d'Hebron University Hospital, Barcelona, Spain
- Vall d'Hebron Institut de Recerca, Barcelona, Spain
- CIBER Enfermedades Respiratorias, Barcelona, Spain
| | - Karina Loor
- Department of Respiratory Medicine, Vall d'Hebron University Hospital, Barcelona, Spain
- Vall d'Hebron Institut de Recerca, Barcelona, Spain
| | - Pau Bosch-Nicolau
- Vall d'Hebron Institut de Recerca, Barcelona, Spain
- Department of Infectious Diseases, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Eva Polverino
- Department of Respiratory Medicine, Vall d'Hebron University Hospital, Barcelona, Spain
- Vall d'Hebron Institut de Recerca, Barcelona, Spain
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14
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Jin KN, Nam BD, Shin J, Hwang SH. [Expert Opinion Questionnaire About Chest CT Scan Using A Negative Pressure Isolation Strecher in COVID-19 Patients: Image Quality and Infection Risk]. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:891-899. [PMID: 37559812 PMCID: PMC10407078 DOI: 10.3348/jksr.2022.0110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/08/2022] [Accepted: 11/13/2022] [Indexed: 08/11/2023]
Abstract
PURPOSE To survey perceptions of certified physicians on the protocol of chest CT in patients with coronavirus (COVID-19) using a negative pressure isolation stretcher (NPIS). MATERIALS AND METHODS This study collected questionnaire responses from a total of 27 certified physicians who had previously performed chest CT with NPIS in COVID-19 isolation hospitals. RESULTS The nine surveyed hospitals performed an average of 116 chest CT examinations with NPIS each year. Of these, an average of 24 cases (21%) were contrast chest CT. Of the 9 pulmonologists we surveyed, 5 (56%) agreed that patients who showed abnormalities in serum D-dimer required contrast chest CT. All 9 surveyed radiologists agreed that the image quality of the chest CT with NPIS was sufficient for CT image interpretation regarding pneumonia or pulmonary embolism. Furthermore, in our 9 surveyed infectionologists, 5 (56%) agreed that a risk of secondary infection in the CT room after temporary opening of NPIS could be prevented through a process of disinfection. CONCLUSION Experienced physicians considered that the effects of NIPS on chest CT image quality was minimal in patients with COVID-19, and the risk of CT room contamination was easily controlled.
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15
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Li H, Drukker K, Hu Q, Whitney HM, Fuhrman JD, Giger ML. Predicting intensive care need for COVID-19 patients using deep learning on chest radiography. J Med Imaging (Bellingham) 2023; 10:044504. [PMID: 37608852 PMCID: PMC10440543 DOI: 10.1117/1.jmi.10.4.044504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 07/12/2023] [Accepted: 08/01/2023] [Indexed: 08/24/2023] Open
Abstract
Purpose Image-based prediction of coronavirus disease 2019 (COVID-19) severity and resource needs can be an important means to address the COVID-19 pandemic. In this study, we propose an artificial intelligence/machine learning (AI/ML) COVID-19 prognosis method to predict patients' needs for intensive care by analyzing chest X-ray radiography (CXR) images using deep learning. Approach The dataset consisted of 8357 CXR exams from 5046 COVID-19-positive patients as confirmed by reverse transcription polymerase chain reaction (RT-PCR) tests for the SARS-CoV-2 virus with a training/validation/test split of 64%/16%/20% on a by patient level. Our model involved a DenseNet121 network with a sequential transfer learning technique employed to train on a sequence of gradually more specific and complex tasks: (1) fine-tuning a model pretrained on ImageNet using a previously established CXR dataset with a broad spectrum of pathologies; (2) refining on another established dataset to detect pneumonia; and (3) fine-tuning using our in-house training/validation datasets to predict patients' needs for intensive care within 24, 48, 72, and 96 h following the CXR exams. The classification performances were evaluated on our independent test set (CXR exams of 1048 patients) using the area under the receiver operating characteristic curve (AUC) as the figure of merit in the task of distinguishing between those COVID-19-positive patients who required intensive care following the imaging exam and those who did not. Results Our proposed AI/ML model achieved an AUC (95% confidence interval) of 0.78 (0.74, 0.81) when predicting the need for intensive care 24 h in advance, and at least 0.76 (0.73, 0.80) for 48 h or more in advance using predictions based on the AI prognostic marker derived from CXR images. Conclusions This AI/ML prediction model for patients' needs for intensive care has the potential to support both clinical decision-making and resource management.
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Affiliation(s)
- Hui Li
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Karen Drukker
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Qiyuan Hu
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Heather M. Whitney
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Jordan D. Fuhrman
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Maryellen L. Giger
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
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16
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Meng F, Kottlors J, Shahzad R, Liu H, Fervers P, Jin Y, Rinneburger M, Le D, Weisthoff M, Liu W, Ni M, Sun Y, An L, Huai X, Móré D, Giannakis A, Kaltenborn I, Bucher A, Maintz D, Zhang L, Thiele F, Li M, Perkuhn M, Zhang H, Persigehl T. AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study. Eur Radiol 2023; 33:4280-4291. [PMID: 36525088 PMCID: PMC9755771 DOI: 10.1007/s00330-022-09335-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 11/03/2022] [Accepted: 11/29/2022] [Indexed: 12/23/2022]
Abstract
OBJECTIVES Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. METHODS We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. RESULTS The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. CONCLUSION This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.
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Affiliation(s)
- Fanyang Meng
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Rahil Shahzad
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Innovative Technology, Philips Healthcare, Aachen, Germany
| | - Haifeng Liu
- Department of Radiology, Wuhan No. 1 Hospital, Wuhan, China
| | - Philipp Fervers
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Yinhua Jin
- Department of Radiology, Ningbo Hwamei Hospital, University of Chinese Academy of Sciences, Wuhan, China
| | - Miriam Rinneburger
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Dou Le
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Mathilda Weisthoff
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Wenyun Liu
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Mengzhe Ni
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Ye Sun
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Liying An
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | | | - Dorottya Móré
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Athanasios Giannakis
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Isabel Kaltenborn
- Institute for Diagnostic and Interventional Radiology, Frankfurt University Hospital, Frankfurt, Germany
| | - Andreas Bucher
- Institute for Diagnostic and Interventional Radiology, Frankfurt University Hospital, Frankfurt, Germany
| | - David Maintz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Lei Zhang
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Frank Thiele
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Innovative Technology, Philips Healthcare, Aachen, Germany
| | - Mingyang Li
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Michael Perkuhn
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Innovative Technology, Philips Healthcare, Aachen, Germany
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China.
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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17
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Sailunaz K, Özyer T, Rokne J, Alhajj R. A survey of machine learning-based methods for COVID-19 medical image analysis. Med Biol Eng Comput 2023; 61:1257-1297. [PMID: 36707488 PMCID: PMC9883138 DOI: 10.1007/s11517-022-02758-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 12/22/2022] [Indexed: 01/29/2023]
Abstract
The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches.
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Affiliation(s)
- Kashfia Sailunaz
- Department of Computer Science, University of Calgary, Calgary, AB, Canada
| | - Tansel Özyer
- Department of Computer Engineering, Ankara Medipol University, Ankara, Turkey
| | - Jon Rokne
- Department of Computer Science, University of Calgary, Calgary, AB, Canada
| | - Reda Alhajj
- Department of Computer Science, University of Calgary, Calgary, AB, Canada.
- Department of Computer Engineering, Istanbul Medipol University, Istanbul, Turkey.
- Department of Health Informatics, University of Southern Denmark, Odense, Denmark.
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18
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Flor N, Fusco S, Blazic I, Sanchez M, Kazerooni EA. Interpretation of chest radiography in patients with known or suspected SARS-CoV-2 infection: what we learnt from comparison with computed tomography. Emerg Radiol 2023; 30:363-376. [PMID: 36435951 PMCID: PMC9702901 DOI: 10.1007/s10140-022-02105-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 11/16/2022] [Indexed: 11/28/2022]
Abstract
Differently from computed tomography (CT), well-defined terminology for chest radiography (CXR) findings and standardized reporting in the setting of known or suspected COVID-19 are still lacking. We propose a revision of CXR major imaging findings in SARS-CoV-2 pneumonia derived from the comparison of CXR and CT, suggesting a precise and standardized terminology for CXR reporting. This description will consider asymptomatic patients, symptomatic patients, and patients with SARS-CoV-2-related pulmonary complications. We suggest using terms such as ground-glass opacities, consolidation, and reticular pattern for the most common findings, and characteristic chest radiographic pattern in presence of one or more of the above-mentioned findings with peripheral and mid-to-lower lung zone distribution.
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Affiliation(s)
- Nicola Flor
- Department of Radiology, ASST Fatebenefratelli Sacco, Luigi Sacco University Hospital, Via GB Grassi 74, 20157, Milan, Italy.
| | - Stefano Fusco
- Postgraduation School in Radiodiagnostics, Facoltà Di Medicina E Chirurgia, Università Degli Studi Di Milano, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Ivana Blazic
- Radiology Department, Clinical Hospital Center Zemun, Belgrade, Serbia
| | - Marcelo Sanchez
- Department of Radiology, CDI, Hospital Clínic, University of Barcelona, Barcelona, Spain
| | - Ella Annabelle Kazerooni
- Departments of Radiology and Internal Medicine, University of Michigan/Michigan Medicine, Ann Arbor, MI, USA
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19
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Nam BD, Hong H, Yoon SH. Diagnostic performance of standardized typical CT findings for COVID-19: a systematic review and meta-analysis. Insights Imaging 2023; 14:96. [PMID: 37222857 DOI: 10.1186/s13244-023-01429-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/14/2023] [Indexed: 05/25/2023] Open
Abstract
OBJECTIVE To meta-analyze diagnostic performance measures of standardized typical CT findings for COVID-19 and examine these measures by region and national income. METHODS MEDLINE and Embase were searched from January 2020 to April 2022 for diagnostic studies using the Radiological Society of North America (RSNA) classification or the COVID-19 Reporting and Data System (CO-RADS) for COVID-19. Patient and study characteristics were extracted. We pooled the diagnostic performance of typical CT findings in the RSNA and CO-RADS systems and interobserver agreement. Meta-regression was performed to examine the effect of potential explanatory factors on the diagnostic performance of the typical CT findings. RESULTS We included 42 diagnostic performance studies with 6777 PCR-positive and 9955 PCR-negative patients from 18 developing and 24 developed countries covering the Americas, Europe, Asia, and Africa. The pooled sensitivity was 70% (95% confidence interval [CI]: 65%, 74%; I2 = 92%), and the pooled specificity was 90% (95% CI 86%, 93%; I2 = 94%) for the typical CT findings of COVID-19. The sensitivity and specificity of the typical CT findings did not differ significantly by national income and the region of the study (p > 0.1, respectively). The pooled interobserver agreement from 19 studies was 0.72 (95% CI 0.63, 0.81; I2 = 99%) for the typical CT findings and 0.67 (95% CI 0.61, 0.74; I2 = 99%) for the overall CT classifications. CONCLUSION The standardized typical CT findings for COVID-19 provided moderate sensitivity and high specificity globally, regardless of region and national income, and were highly reproducible between radiologists. CRITICAL RELEVANCE STATEMENT Standardized typical CT findings for COVID-19 provided a reproducible high diagnostic accuracy globally. KEY POINTS Standardized typical CT findings for COVID-19 provide high sensitivity and specificity. Typical CT findings show high diagnosability regardless of region or income. The interobserver agreement for typical findings of COVID-19 is substantial.
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Affiliation(s)
- Bo Da Nam
- Department of Radiology, Soonchunhyang University College of Medicine, Soonchunhyang University Seoul Hospital, Seoul, Republic of Korea
| | - Hyunsook Hong
- Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
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20
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Yoo SJ, Kim H, Witanto JN, Inui S, Yoon JH, Lee KD, Choi YW, Goo JM, Yoon SH. Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographs. Eur J Radiol 2023; 164:110858. [PMID: 37209462 DOI: 10.1016/j.ejrad.2023.110858] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 04/10/2023] [Accepted: 04/29/2023] [Indexed: 05/22/2023]
Abstract
PURPOSE To develop a generative adversarial network (GAN) to quantify COVID-19 pneumonia on chest radiographs automatically. MATERIALS AND METHODS This retrospective study included 50,000 consecutive non-COVID-19 chest CT scans in 2015-2017 for training. Anteroposterior virtual chest, lung, and pneumonia radiographs were generated from whole, segmented lung, and pneumonia pixels from each CT scan. Two GANs were sequentially trained to generate lung images from radiographs and to generate pneumonia images from lung images. GAN-driven pneumonia extent (pneumonia area/lung area) was expressed from 0% to 100%. We examined the correlation of GAN-driven pneumonia extent with semi-quantitative Brixia X-ray severity score (one dataset, n = 4707) and quantitative CT-driven pneumonia extent (four datasets, n = 54-375), along with analyzing a measurement difference between the GAN and CT extents. Three datasets (n = 243-1481), where unfavorable outcomes (respiratory failure, intensive care unit admission, and death) occurred in 10%, 38%, and 78%, respectively, were used to examine the predictive power of GAN-driven pneumonia extent. RESULTS GAN-driven radiographic pneumonia was correlated with the severity score (0.611) and CT-driven extent (0.640). 95% limits of agreements between GAN and CT-driven extents were -27.1% to 17.4%. GAN-driven pneumonia extent provided odds ratios of 1.05-1.18 per percent for unfavorable outcomes in the three datasets, with areas under the receiver operating characteristic curve (AUCs) of 0.614-0.842. When combined with demographic information only and with both demographic and laboratory information, the prediction models yielded AUCs of 0.643-0.841 and 0.688-0.877, respectively. CONCLUSION The generative adversarial network automatically quantified COVID-19 pneumonia on chest radiographs and identified patients with unfavorable outcomes.
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Affiliation(s)
- Seung-Jin Yoo
- Department of Radiology, Hanyang University Medical Center, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Hyungjin Kim
- Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea
| | | | - Shohei Inui
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Radiology, Japan Self-Defense Forces Central Hospital, Tokyo, Japan
| | - Jeong-Hwa Yoon
- Institute of Health Policy and Management, Medical Research Center, Seoul National University, Seoul, South Korea
| | - Ki-Deok Lee
- Division of Infectious diseases, Department of Internal Medicine, Myongji Hospital, Goyang, Korea
| | - Yo Won Choi
- Department of Radiology, Hanyang University Medical Center, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea; MEDICALIP Co. Ltd., Seoul, Korea
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21
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Sun Y, Salerno S, He X, Pan Z, Yang E, Sujimongkol C, Song J, Wang X, Han P, Kang J, Sjoding MW, Jolly S, Christiani DC, Li Y. Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality. Sci Rep 2023; 13:7318. [PMID: 37147440 PMCID: PMC10161188 DOI: 10.1038/s41598-023-34559-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 05/03/2023] [Indexed: 05/07/2023] Open
Abstract
As portable chest X-rays are an efficient means of triaging emergent cases, their use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and investigated the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with a higher comorbidity burden. Important features included age, oxygen saturation, blood pressure, and certain comorbid conditions, as well as image features related to the intensity and variability of pixel distribution. Thus, widely available chest X-rays, in conjunction with clinical information, may be predictive of survival outcomes of patients with COVID-19, especially older, sicker patients, and can aid in disease management by providing additional information.
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Affiliation(s)
- Yuming Sun
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Stephen Salerno
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Xinwei He
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Ziyang Pan
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Eileen Yang
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Chinakorn Sujimongkol
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Jiyeon Song
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Xinan Wang
- Department of Environmental Health and Epidemiology, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Peisong Han
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Michael W Sjoding
- Division of Pulmonary and Critical Care, Department of Internal Medicine, University of Michigan Medical School, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan Rogel Cancer Center, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - David C Christiani
- Department of Environmental Health and Epidemiology, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care, Department of Internal Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Yi Li
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
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22
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Zamora-Mendoza BN, Sandoval-Flores H, Rodríguez-Aguilar M, Jiménez-González C, Alcántara-Quintana LE, Berumen-Rodríguez AA, Flores-Ramírez R. Determination of global chemical patterns in exhaled breath for the discrimination of lung damage in postCOVID patients using olfactory technology. Talanta 2023; 256:124299. [PMID: 36696734 DOI: 10.1016/j.talanta.2023.124299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/18/2023] [Accepted: 01/20/2023] [Indexed: 01/21/2023]
Abstract
The objective of this work was to evaluate the use of an electronic nose and chemometric analysis to discriminate global patterns of volatile organic compounds (VOCs) in breath of postCOVID syndrome patients with pulmonary sequelae. A cross-sectional study was performed in two groups, the group 1 were subjects recovered from COVID-19 without lung damage and the group 2 were subjects recovered from COVID-19 with impaired lung function. The VOCs analysis was executed using a Cyranose 320 electronic nose with 32 sensors, applying principal component analysis (PCA), Partial Least Square-Discriminant Analysis, random forest, canonical discriminant analysis (CAP) and the diagnostic power of the test was evaluated using the ROC (Receiver Operating Characteristic) curve. A total of 228 participants were obtained, for the postCOVID group there are 157 and 71 for the control group, the chemometric analysis results indicate in the PCA an 84% explanation of the variability between the groups, the PLS-DA indicates an observable separation between the groups and 10 sensors related to this separation, by random forest, a classification error was obtained for the control group of 0.090 and for the postCOVID group of 0.088 correct classification. The CAP model showed 83.8% of correct classification and the external validation of the model showed 80.1% of correct classification. Sensitivity and specificity reached 88.9% (73.9%-96.9%) and 96.9% (83.7%-99.9%) respectively. It is considered that this technology can be used to establish the starting point in the evaluation of lung damage in postCOVID patients with pulmonary sequelae.
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Affiliation(s)
- Blanca Nohemí Zamora-Mendoza
- Faculty of Medicine-Center for Applied Research on Environment and Health (CIAAS), Autonomous University of San Luis Potosí, Avenida Sierra Leona No. 550, CP 78210, Colonia Lomas Segunda Sección, San Luis Potosí, Mexico
| | - Hannia Sandoval-Flores
- Faculty of Medicine-Center for Applied Research on Environment and Health (CIAAS), Autonomous University of San Luis Potosí, Avenida Sierra Leona No. 550, CP 78210, Colonia Lomas Segunda Sección, San Luis Potosí, Mexico
| | | | - Carlos Jiménez-González
- Faculty of Medicine-Center for Applied Research on Environment and Health (CIAAS), Autonomous University of San Luis Potosí, Avenida Sierra Leona No. 550, CP 78210, Colonia Lomas Segunda Sección, San Luis Potosí, Mexico
| | - Luz Eugenia Alcántara-Quintana
- CONACYT Research Fellow, Coordination for Innovation and Application of Science and Technology (CIACYT), Autonomous University of San Luis Potosí, Avenida Sierra Leona No. 550, CP 78210, Colonia Lomas Segunda Sección, San Luis Potosí, Mexico
| | - Alejandra Abigail Berumen-Rodríguez
- Faculty of Medicine-Center for Applied Research on Environment and Health (CIAAS), Autonomous University of San Luis Potosí, Avenida Sierra Leona No. 550, CP 78210, Colonia Lomas Segunda Sección, San Luis Potosí, Mexico
| | - Rogelio Flores-Ramírez
- CONACYT Research Fellow, Coordination for Innovation and Application of Science and Technology (CIACYT), Autonomous University of San Luis Potosí, Avenida Sierra Leona No. 550, CP 78210, Colonia Lomas Segunda Sección, San Luis Potosí, Mexico.
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23
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Spogis J, Fusco S, Hagen F, Kaufmann S, Malek N, Hoffmann T. Repeated Lung Ultrasound versus Chest X-ray-Which One Predicts Better Clinical Outcome in COVID-19? Tomography 2023; 9:706-716. [PMID: 36961015 PMCID: PMC10037641 DOI: 10.3390/tomography9020056] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/06/2023] [Accepted: 03/14/2023] [Indexed: 03/25/2023] Open
Abstract
The purpose of this study was to evaluate whether changes in repeated lung ultrasound (LUS) or chest X-ray (CXR) of coronavirus disease 2019 (COVID-19) patients can predict the development of severe disease and the need for treatment in the intensive care unit (ICU). In this prospective monocentric study, COVID-19 patients received standardized LUS and CXR at day 1, 3 and 5. Scores for changes in LUS (LUS score) and CXR (RALE and M-RALE) were calculated and compared. Intra-class correlation was calculated for two readers of CXR and ROC analysis to evaluate the best discriminator for the need for ICU treatment. A total of 30 patients were analyzed, 26 patients with follow-up LUS and CXR. Increase in M-RALE between baseline and follow-up 1 was significantly higher in patients with need for ICU treatment in the further hospital stay (p = 0.008). Both RALE and M-RALE significantly correlated with LUS score (r = 0.5, p < 0.0001). ROC curves with need for ICU treatment as separator were not significantly different for changes in M-RALE (AUC: 0.87) and LUS score (AUC: 0.79), both being good discriminators. ICC was moderate for RALE (0.56) and substantial for M-RALE (0.74). The present study demonstrates that both follow-up LUS and CXR are powerful tools to track the evolution of COVID-19, and can be used equally as predictors for the need for ICU treatment.
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Affiliation(s)
- Jakob Spogis
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany
| | - Stefano Fusco
- Department of Internal Medicine, Eberhard-Karls-University, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany
| | - Florian Hagen
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany
| | - Sascha Kaufmann
- Department of Diagnostic and Interventional Radiology, Siloah St. Trudpert Klinikum, Wilferdinger Straße 67, 75179 Pforzheim, Germany
| | - Nisar Malek
- Department of Internal Medicine, Eberhard-Karls-University, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany
| | - Tatjana Hoffmann
- Department of Internal Medicine, Eberhard-Karls-University, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany
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Yang D, Ren G, Ni R, Huang YH, Lam NFD, Sun H, Wan SBN, Wong MFE, Chan KK, Tsang HCH, Xu L, Wu TC, Kong FM(S, Wáng YXJ, Qin J, Chan LWC, Ying M, Cai J. Deep learning attention-guided radiomics for COVID-19 chest radiograph classification. Quant Imaging Med Surg 2023; 13:572-584. [PMID: 36819269 PMCID: PMC9929417 DOI: 10.21037/qims-22-531] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 09/17/2022] [Indexed: 11/23/2022]
Abstract
Background Accurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR). Methods In this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features. Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation. Results Using the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19). Conclusions A two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.
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Affiliation(s)
- Dongrong Yang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ruiyan Ni
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ngo Fung Daniel Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Hongfei Sun
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Shiu Bun Nelson Wan
- Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China
| | - Man Fung Esther Wong
- Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China
| | - King Kwong Chan
- Department of Radiology and Imaging, Queen Elizabeth Hospital, Hong Kong, China
| | | | - Lu Xu
- Department of Radiology and Imaging, Queen Elizabeth Hospital, Hong Kong, China
| | - Tak Chiu Wu
- Department of Radiology and Imaging, Queen Elizabeth Hospital, Hong Kong, China
| | | | - Yì Xiáng J. Wáng
- Deparment of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
| | - Jing Qin
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China
| | - Lawrence Wing Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Michael Ying
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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Blazic I, Cogliati C, Flor N, Frija G, Kawooya M, Umbrello M, Ali S, Baranne ML, Cho YJ, Pitcher R, Vollmer I, van Deventer E, del Rosario Perez M. The use of lung ultrasound in COVID-19. ERJ Open Res 2023; 9:00196-2022. [PMID: 36628270 PMCID: PMC9548241 DOI: 10.1183/23120541.00196-2022] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 09/22/2022] [Indexed: 01/13/2023] Open
Abstract
This review article addresses the role of lung ultrasound in patients with coronavirus disease 2019 (COVID-19) for diagnosis and disease management. As a simple imaging procedure, lung ultrasound contributes to the early identification of patients with clinical conditions suggestive of COVID-19, supports decisions about hospital admission and informs therapeutic strategy. It can be performed in various clinical settings (primary care facilities, emergency departments, hospital wards, intensive care units), but also in outpatient settings using portable devices. The article describes typical lung ultrasound findings for COVID-19 pneumonia (interstitial pattern, pleural abnormalities and consolidations), as one component of COVID-19 diagnostic workup that otherwise includes clinical and laboratory evaluation. Advantages and limitations of lung ultrasound use in COVID-19 are described, along with equipment requirements and training needs. To infer on the use of lung ultrasound in different regions, a literature search was performed using key words "COVID-19", "lung ultrasound" and "imaging". Lung ultrasound is a noninvasive, rapid and reproducible procedure; can be performed at the point of care; requires simple sterilisation; and involves non-ionising radiation, allowing repeated exams on the same patient, with special benefit in children and pregnant women. However, physical proximity between the patient and the ultrasound operator is a limitation in the current pandemic context, emphasising the need to implement specific infection prevention and control measures. Availability of qualified staff adequately trained to perform lung ultrasound remains a major barrier to lung ultrasound utilisation. Training, advocacy and awareness rising can help build up capacities of local providers to facilitate lung ultrasound use for COVID-19 management, in particular in low- and middle-income countries.
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Affiliation(s)
- Ivana Blazic
- Radiology Department, Clinical Hospital Center Zemun, Belgrade, Serbia
| | - Chiara Cogliati
- Internal Medicine, L. Sacco Hospital, ASST Fatebenefratelli-Sacco, Milan, Italy
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Nicola Flor
- Unità Operativa di Radiologia, Luigi Sacco University Hospital, Milan, Italy
| | - Guy Frija
- Université de Paris, International Society of Radiology, Paris, France
| | - Michael Kawooya
- Ernest Cook Ultrasound Research and Education Institute (ECUREI), Kampala, Uganda
| | - Michele Umbrello
- SC Anestesia e Rianimazione II, Ospedale San Carlo Borromeo, ASST Santi Paolo e Carlo – Polo Universitario, Milan, Italy
| | - Sam Ali
- ECUREI, Mengo Hospital, Kampala, Uganda
| | - Marie-Laure Baranne
- Assistance Publique – Hôpitaux de Paris, Paris Institute for Clinical Ultrasound, Paris, France
| | - Young-Jae Cho
- South Korea/Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seoul, South Korea
| | - Richard Pitcher
- Division of Radiodiagnosis, Department of Medical Imaging and Clinical Oncology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
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Abuzaid MM, Elshami W, Tekin HO. Infection control and radiation safety practices in the radiology department during the COVID-19 outbreak. PLoS One 2022; 17:e0279607. [PMID: 36574426 PMCID: PMC9794035 DOI: 10.1371/journal.pone.0279607] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 12/10/2022] [Indexed: 12/29/2022] Open
Abstract
RATIONALE AND OBJECTIVES Radiology personnel must have good knowledge, experience and adherence to radiation protection and infection control practices to ensure patient safety and prevent the further spread of the COVID-19 virus. This study analysed compliance and adherence to radiation protection and infection control during COVID-19 mobile radiography. METHODS A cross-sectional using online survey was conducted from September to December 2021. Data on demographic characteristics, adherence to radiation protection and infection control practice were collected during mobile radiography for COVID-19 patients in the study. A random sample of the radiographers working in COVID-19 centres in the United Arab Emirates. RESULTS Responses were received from 140 participants, with a response rate of 87.5%. Females were the predominant participants (n = 81; 58%). Participants aged ages between 18-25 years (n = 46; 33%) and 26-35 years (n = 42; 30%), (n = 57; 41%) had less than five years of experience, followed by participants who had more than 15 years (n = 38; 27%). Most participants (n = 81; 57.9%) stated that they performed approximately 1-5 suspected or confirmed COVID-19 cases daily. The participants had moderate to high adherence to radiation protection, with a mean and standard deviation of 42.3 ± 6.28. Additionally, infection control adherence was high, with 82% of the participants showing high adherence. CONCLUSION Continuous guidance, training and follow-up are recommended to increase adherence and compliance to radiation protection and infection control compliance. Educational institutions and professional organisations must collaborate to provide structured training programmes for radiology practitioners to overcome the practice and knowledge gap.
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Affiliation(s)
- Mohamed M Abuzaid
- Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, Sharjah, UAE
| | - Wiam Elshami
- Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, Sharjah, UAE
| | - H O Tekin
- Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, Sharjah, UAE
- Istinye University, Faculty of Engineering and Natural Sciences, Computer Engineering Department, Istanbul, Turkey
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27
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Abstract
The sudden contrast dye shortage, precipitated by a temporary forced closure of healthcare plant, has limited the supply of iodinated contrast media to Australia. Furthering the impact of the coronavirus disease 2019 pandemic, this new crisis has increased burden on the radiology system. Lessons from the strategies applied during the shortage should be used as building blocks as safeguards for the future. A pragmatic approach to education and training is required in an ever-changing environment. Our relationships between medical specialties and manufacturers are paramount to maintaining an effective workflow. An ongoing commitment to a strong workforce will be the backbone to overcome another challenge in these uncertain times.
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Affiliation(s)
- Christiaan Yu
- Department of Respiratory Medicine, Alfred Health, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne Australia
- * Correspondence: Christiaan Yu, Respiratory and Sleep Consultant, Department of Respiratory Medicine, 55 Commercial Road, Melbourne VIC 3004, Australia (e-mail: )
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Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust. PLoS One 2022; 17:e0278487. [PMID: 36548288 PMCID: PMC9778629 DOI: 10.1371/journal.pone.0278487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 11/17/2022] [Indexed: 12/24/2022] Open
Abstract
Due to the severity and speed of spread of the ongoing Covid-19 pandemic, fast but accurate diagnosis of Covid-19 patients has become a crucial task. Achievements in this respect might enlighten future efforts for the containment of other possible pandemics. Researchers from various fields have been trying to provide novel ideas for models or systems to identify Covid-19 patients from different medical and non-medical data. AI-based researchers have also been trying to contribute to this area by mostly providing novel approaches of automated systems using convolutional neural network (CNN) and deep neural network (DNN) for Covid-19 detection and diagnosis. Due to the efficiency of deep learning (DL) and transfer learning (TL) models in classification and segmentation tasks, most of the recent AI-based researches proposed various DL and TL models for Covid-19 detection and infected region segmentation from chest medical images like X-rays or CT images. This paper describes a web-based application framework for Covid-19 lung infection detection and segmentation. The proposed framework is characterized by a feedback mechanism for self learning and tuning. It uses variations of three popular DL models, namely Mask R-CNN, U-Net, and U-Net++. The models were trained, evaluated and tested using CT images of Covid patients which were collected from two different sources. The web application provide a simple user friendly interface to process the CT images from various resources using the chosen models, thresholds and other parameters to generate the decisions on detection and segmentation. The models achieve high performance scores for Dice similarity, Jaccard similarity, accuracy, loss, and precision values. The U-Net model outperformed the other models with more than 98% accuracy.
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Clinical Characteristics and Management of Patients with a Suspected COVID-19 Infection in Emergency Departments: A European Retrospective Multicenter Study. J Pers Med 2022; 12:jpm12122085. [PMID: 36556305 PMCID: PMC9787691 DOI: 10.3390/jpm12122085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/21/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
Background: Our aim is to describe and compare the profile and outcome of patients attending the ED with a confirmed COVID-19 infection with patients with a suspected COVID-19 infection. Methods: We conducted a multicentric retrospective study including adults who were seen in 21 European emergency departments (ED) with suspected COVID-19 between 9 March and 8 April 2020. Patients with either a clinical suspicion of COVID-19 or confirmed COVID-19, detected using either a RT-PCR or a chest CT scan, formed the C+ group. Patients with non-confirmed COVID-19 (C− group) were defined as patients with a clinical presentation in the ED suggestive of COVID-19, but if tests were performed, they showed a negative RT-PCR and/or a negative chest CT scan. Results: A total of 7432 patients were included in the analysis: 1764 (23.7%) in the C+ group and 5668 (76.3%) in the C− group. The population was older (63.8 y.o. ±17.5 vs. 51.8 y.o. +/− 21.1, p < 0.01), with more males (54.6% vs. 46.1%, p < 0.01) in the C+ group. Patients in the C+ group had more chronic diseases. Half of the patients (n = 998, 56.6%) in the C+ group needed oxygen, compared to only 15% in the C− group (n = 877). Two-thirds of patients from the C+ group were hospitalized in ward (n = 1128, 63.9%), whereas two-thirds of patients in the C− group were discharged after their ED visit (n = 3883, 68.5%). Conclusion: Our study was the first in Europe to examine the emergency department’s perspective on the management of patients with a suspected COVID-19 infection. We showed an overall more critical clinical situation group of patients with a confirmed COVID-19 infection.
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Galzin E, Roche L, Vlachomitrou A, Nempont O, Carolus H, Schmidt-Richberg A, Jin P, Rodrigues P, Klinder T, Richard JC, Tazarourte K, Douplat M, Sigal A, Bouscambert-Duchamp M, Si-Mohamed SA, Gouttard S, Mansuy A, Talbot F, Pialat JB, Rouvière O, Milot L, Cotton F, Douek P, Duclos A, Rabilloud M, Boussel L. Additional value of chest CT AI-based quantification of lung involvement in predicting death and ICU admission for COVID-19 patients. RESEARCH IN DIAGNOSTIC AND INTERVENTIONAL IMAGING 2022; 4:100018. [PMID: 37284031 PMCID: PMC9716289 DOI: 10.1016/j.redii.2022.100018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 11/15/2022] [Indexed: 12/03/2022]
Abstract
Objectives We evaluated the contribution of lung lesion quantification on chest CT using a clinical Artificial Intelligence (AI) software in predicting death and intensive care units (ICU) admission for COVID-19 patients. Methods For 349 patients with positive COVID-19-PCR test that underwent a chest CT scan at admittance or during hospitalization, we applied the AI for lung and lung lesion segmentation to obtain lesion volume (LV), and LV/Total Lung Volume (TLV) ratio. ROC analysis was used to extract the best CT criterion in predicting death and ICU admission. Two prognostic models using multivariate logistic regressions were constructed to predict each outcome and were compared using AUC values. The first model ("Clinical") was based on patients' characteristics and clinical symptoms only. The second model ("Clinical+LV/TLV") included also the best CT criterion. Results LV/TLV ratio demonstrated best performance for both outcomes; AUC of 67.8% (95% CI: 59.5 - 76.1) and 81.1% (95% CI: 75.7 - 86.5) respectively. Regarding death prediction, AUC values were 76.2% (95% CI: 69.9 - 82.6) and 79.9% (95%IC: 74.4 - 85.5) for the "Clinical" and the "Clinical+LV/TLV" models respectively, showing significant performance increase (+ 3.7%; p-value<0.001) when adding LV/TLV ratio. Similarly, for ICU admission prediction, AUC values were 74.9% (IC 95%: 69.2 - 80.6) and 84.8% (IC 95%: 80.4 - 89.2) respectively corresponding to significant performance increase (+ 10%: p-value<0.001). Conclusions Using a clinical AI software to quantify the COVID-19 lung involvement on chest CT, combined with clinical variables, allows better prediction of death and ICU admission.
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Affiliation(s)
- Eloise Galzin
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
| | - Laurent Roche
- Department of Biostatistics, Hospices Civils de Lyon, Lyon F-69003, France
- Université de Lyon, Lyon F-69000, France
- Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, CNRS, UMR5558, Equipe Biostatistique-Santé, Villeurbanne F-69622, France
| | - Anna Vlachomitrou
- Philips France, 33 rue de Verdun, CS 60 055, Suresnes Cedex 92156, France
| | - Olivier Nempont
- Philips France, 33 rue de Verdun, CS 60 055, Suresnes Cedex 92156, France
| | - Heike Carolus
- Philips Research, Röntgenstrasse 24-26, Hamburg D-22335, Germany
| | | | - Peng Jin
- Philips Medical Systems Nederland BV (Philips Healthcare), the Netherlands
| | - Pedro Rodrigues
- Philips Medical Systems Nederland BV (Philips Healthcare), the Netherlands
| | - Tobias Klinder
- Philips Research, Röntgenstrasse 24-26, Hamburg D-22335, Germany
| | - Jean-Christophe Richard
- Department of Critical Care Medicine, Hôpital De La Croix Rousse, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | - Karim Tazarourte
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France
- Emergency department and SAMU 69, Hospices civils de Lyon, France
| | - Marion Douplat
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France
- Emergency department and SAMU 69, Hospices civils de Lyon, France
| | - Alain Sigal
- Emergency department and SAMU 69, Hospices civils de Lyon, France
| | - Maude Bouscambert-Duchamp
- Laboratoire de Virologie, Institut des Agents Infectieux de Lyon, Centre National de Référence des virus respiratoires France Sud, Centre de Biologie et de Pathologie Nord, Hospices Civils de Lyon, Lyon F-69317, France
- Université de Lyon, Virpath, CIRI, INSERM U1111, CNRS UMR5308, ENS Lyon, Université Claude Bernard Lyon 1, Lyon F-69372, France
| | - Salim Aymeric Si-Mohamed
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | | | - Adeline Mansuy
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
| | - François Talbot
- Department of Information Technology, Hospices Civils de Lyon, Lyon, France
| | - Jean-Baptiste Pialat
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | - Olivier Rouvière
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- LabTAU INSERM U1032, Lyon, France
| | - Laurent Milot
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- LabTAU INSERM U1032, Lyon, France
| | - François Cotton
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | - Philippe Douek
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | - Antoine Duclos
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France
| | - Muriel Rabilloud
- Department of Biostatistics, Hospices Civils de Lyon, Lyon F-69003, France
- Université de Lyon, Lyon F-69000, France
- Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, CNRS, UMR5558, Equipe Biostatistique-Santé, Villeurbanne F-69622, France
| | - Loic Boussel
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
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Taheriyan M, Ayyoubzadeh SM, Ebrahimi M, R. Niakan Kalhori S, Abooei AH, Gholamzadeh M, Ayyoubzadeh SM. Prediction of COVID-19 Patients' Survival by Deep Learning Approaches. Med J Islam Repub Iran 2022; 36:144. [PMID: 36569399 PMCID: PMC9774992 DOI: 10.47176/mjiri.36.144] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Indexed: 12/24/2022] Open
Abstract
Background: Despite many studies done to predict severe coronavirus 2019 (COVID-19) patients, there is no applicable clinical prediction model to predict and distinguish severe patients early. Based on laboratory and demographic data, we have developed and validated a deep learning model to predict survival and assist in the triage of COVID-19 patients in the early stages. Methods: This retrospective study developed a survival prediction model based on the deep learning method using demographic and laboratory data. The database consisted of data from 487 patients with COVID-19 diagnosed by the reverse transcription-polymerase chain reaction test and admitted to Imam Khomeini hospital affiliated to Tehran University of Medical Sciences from February 21, 2020, to June 24, 2020. Results: The developed model achieved an area under the curve (AUC) of 0.96 for survival prediction. The results demonstrated the developed model provided high precision (0.95, 0.93), recall (0.90,0.97), and F1-score (0.93,0.95) for low- and high-risk groups. Conclusion: The developed model is a deep learning-based, data-driven prediction tool that can predict the survival of COVID-19 patients with an AUC of 0.96. This model helps classify admitted patients into low-risk and high-risk groups and helps triage patients in the early stages.
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Affiliation(s)
- Moloud Taheriyan
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Mehdi Ebrahimi
- Department of Internal Medicine, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Sharareh R. Niakan Kalhori
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran, Peter L. Reichertz Institute for Medical Informatics (PLRI) of Technical University of Braunschweig and Hannover Medical School, Braunschweig, Germany
| | - Amir Hossien Abooei
- Department of Laboratory Sciences, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Marsa Gholamzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran, Thoracic Research Center, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Mohammad Ayyoubzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran, Corresponding author:Seyed Mohammad Ayyoubzadeh,
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Kim YJ. Machine Learning Model Based on Radiomic Features for Differentiation between COVID-19 and Pneumonia on Chest X-ray. SENSORS (BASEL, SWITZERLAND) 2022; 22:6709. [PMID: 36081170 PMCID: PMC9460643 DOI: 10.3390/s22176709] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 06/15/2023]
Abstract
Machine learning approaches are employed to analyze differences in real-time reverse transcription polymerase chain reaction scans to differentiate between COVID-19 and pneumonia. However, these methods suffer from large training data requirements, unreliable images, and uncertain clinical diagnosis. Thus, in this paper, we used a machine learning model to differentiate between COVID-19 and pneumonia via radiomic features using a bias-minimized dataset of chest X-ray scans. We used logistic regression (LR), naive Bayes (NB), support vector machine (SVM), k-nearest neighbor (KNN), bagging, random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM) to differentiate between COVID-19 and pneumonia based on training data. Further, we used a grid search to determine optimal hyperparameters for each machine learning model and 5-fold cross-validation to prevent overfitting. The identification performances of COVID-19 and pneumonia were compared with separately constructed test data for four machine learning models trained using the maximum probability, contrast, and difference variance of the gray level co-occurrence matrix (GLCM), and the skewness as input variables. The LGBM and bagging model showed the highest and lowest performances; the GLCM difference variance showed a high overall effect in all models. Thus, we confirmed that the radiomic features in chest X-rays can be used as indicators to differentiate between COVID-19 and pneumonia using machine learning.
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Affiliation(s)
- Young Jae Kim
- Department of Biomedical Engineering, Gachon University, 21, Namdong-daero 774 beon-gil, Namdong-gu, Inchon 21936, Korea
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Vliegenthart R, Fouras A, Jacobs C, Papanikolaou N. Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry. Respirology 2022; 27:818-833. [PMID: 35965430 PMCID: PMC9546393 DOI: 10.1111/resp.14344] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 07/08/2022] [Indexed: 12/11/2022]
Abstract
In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of ‘non visual’ markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID‐19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x‐ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra‐low‐dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon‐counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X‐ray velocimetry integrates x‐ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation. See relatedEditorial
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Affiliation(s)
- Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.,Data Science in Health (DASH), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Colin Jacobs
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Nickolas Papanikolaou
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.,AI Hub, The Royal Marsden NHS Foundation Trust, London, UK.,The Institute of Cancer Research, London, UK
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Hammer MM, Raptis CA, Henry TS, Bhalla S. COVID-19 in the Radiology Literature: A Look Back. Radiol Cardiothorac Imaging 2022; 4:e220102. [PMID: 35935812 PMCID: PMC9341167 DOI: 10.1148/ryct.220102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/13/2022] [Accepted: 06/17/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Mark M. Hammer
- From the Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (M.M.H.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (C.A.R., S.B.); and Department of Radiology, Duke University School of Medicine, Durham, NC (T.S.H.)
| | - Constantine A. Raptis
- From the Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (M.M.H.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (C.A.R., S.B.); and Department of Radiology, Duke University School of Medicine, Durham, NC (T.S.H.)
| | - Travis S. Henry
- From the Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (M.M.H.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (C.A.R., S.B.); and Department of Radiology, Duke University School of Medicine, Durham, NC (T.S.H.)
| | - Sanjeev Bhalla
- From the Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (M.M.H.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (C.A.R., S.B.); and Department of Radiology, Duke University School of Medicine, Durham, NC (T.S.H.)
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Soedarsono S, Yunita D, Ayu Lirani E, Kartika Sari R, Indrawan Pratama Y, Listiati A, Supriyanto B. The Role of Simple Blood Tests and a Modified Chest X-Ray Scoring System in Assessing the Severity Disease and Mortality Risk in COVID-19 Patients in a Secondary Hospital, Indonesia. Int J Gen Med 2022; 15:5891-5900. [PMID: 35795303 PMCID: PMC9252582 DOI: 10.2147/ijgm.s367305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 06/21/2022] [Indexed: 01/08/2023] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) has resulted in millions of mortality cases and significant incremental costs to the healthcare system. Examination of CRP and D-dimer were considered to have higher costs, and the use of simple hematological parameters such as lymphocyte, neutrophil, and white blood cell (WBC) which have more affordable costs would be cost-saving. Radiological imaging complements clinical evaluation and laboratory parameters for managing COVID-19 patients. Therefore, categorizing patients into severe or non-severe becomes more defined, allowing for earlier interventions and decisions of hospital admission or being referred to a tertiary hospital. Purpose To evaluate the variables correlated with poor outcomes in COVID-19 patients. Patients and Methods This was a retrospective study on COVID-19 patients in a secondary referral hospital in treating COVID-19 in Indonesia. Demographic, clinical data, laboratory parameters, CXR (analyzed using a modified scoring system), and prognosis were collected through electronic nursing and medical records. Results This study included 476 hospitalized COVID-19 patients. Severe patients were commonly found with older age (median of 57 vs 40), dyspnea (percentage of 85.2% vs 20.5%), higher CXR score (median of 7 vs 5), higher levels of neutrophil (median of 79.9 vs 68.3), and lower lymphocyte levels (median of 13.4 vs 22.7), compared to non-severe patients. These variables were known to increase the odds of severe disease. Older age (median of 57 vs 48), SpO2 <94% room air (percentage of 87.4% vs 31.5%), higher CXR score (median of 8 vs 5), and higher respiratory rate (median of 25 vs 20) were found higher in death patients and were known to increase the odds of death outcome. Conclusion The simple blood tests (neutrophil and lymphocyte) and modified CXR scoring system are useful in risk stratification for severe disease and mortality in COVID-19 patients to decide the earlier interventions and treatment.
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Affiliation(s)
- Soedarsono Soedarsono
- Sub-Pulmonology Department of Internal Medicine, Faculty of Medicine, Hang Tuah University, Surabaya, East Java, Indonesia
| | - Deri Yunita
- Medical and Health Service Management, Petrokimia Gresik Hospital, Gresik, East Java, Indonesia
| | - Emma Ayu Lirani
- Emergency Installation, Petrokimia Gresik Hospital, Gresik, East Java, Indonesia
| | - Robitha Kartika Sari
- Emergency Installation, Petrokimia Gresik Hospital, Gresik, East Java, Indonesia
| | | | - Afifah Listiati
- Emergency Installation, Petrokimia Gresik Hospital, Gresik, East Java, Indonesia
| | - Bambang Supriyanto
- Department of Radiology, Faculty of Medicine, Universitas Airlangga, Surabaya, East Java, Indonesia
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Song J, Patel J, Khatri R, Nadpara N, Malik Z, Parkman HP. Gastrointestinal symptoms in patients hospitalized with COVID-19: Prevalence and outcomes. Medicine (Baltimore) 2022; 101:e29374. [PMID: 35758370 PMCID: PMC9276248 DOI: 10.1097/md.0000000000029374] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 05/10/2022] [Indexed: 01/09/2023] Open
Abstract
To characterize outcomes in patients hospitalized with coronavirus disease 2019 (COVID-19) who present with gastrointestinal (GI) symptoms.Clinical outcomes in patients with COVID-19 associated with GI symptoms have been inconsistent in the literature.The study design is a retrospective analysis of patients, age 18 years or older, admitted to the hospital after testing positive for COVID-19. Clinical outcomes included intensive care unit requirements, rates of discharges to home, rates of discharges to outside facilities, and mortality.Seven hundred fifty patients met the inclusion criteria. Three hundred seventy three (49.7%) patients presented with at least one GI symptom and 377 (50.3%) patients presented with solely non-GI symptoms. Patients who presented with at least one GI symptom had significantly lower ICU requirements (17.4% vs 20.2%), higher rates of discharges home (77.2% vs 67.4%), lower rates of discharges to other facilities (16.4% vs 22.8%), and decreased mortality (6.4% vs 9.8%) compared with patients with non-GI symptoms. However, patients who presented with solely GI symptoms had significantly higher ICU requirements (23.8% vs 17.0%), lower rates of discharges home (52.4% vs 78.7%), higher rates of discharges to facilities (28.6% vs 15.6%), and higher mortality (19.0% vs 5.7%) compared with those with mixed GI and non-GI symptoms.Although patients with COVID-19 requiring hospitalization with GI symptoms did better than those without GI symptoms, those with isolated GI symptoms without extra-GI symptoms had worse clinical outcomes. COVID-19 should be considered in patients who present with new onset or worsening diarrhea, nausea, vomiting, and abdominal pain even without pulmonary symptoms.
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Affiliation(s)
- Jun Song
- Temple University Hospital, Department of Medicine, Temple University School of Medicine, 3401 North Broad Street, Philadelphia, PA
| | - Jay Patel
- Temple University Hospital, Department of Medicine, Temple University School of Medicine, 3401 North Broad Street, Philadelphia, PA
| | - Rishabh Khatri
- Temple University Hospital, Department of Medicine, Temple University School of Medicine, 3401 North Broad Street, Philadelphia, PA
| | - Neil Nadpara
- Temple University Hospital, Department of Medicine, Temple University School of Medicine, 3401 North Broad Street, Philadelphia, PA
| | - Zubair Malik
- Temple University Hospital, Department of Medicine, Section of Gastroenterology and Hepatology, Temple University School of Medicine, 3401 North Broad Street, Philadelphia, PA
| | - Henry P. Parkman
- Temple University Hospital, Department of Medicine, Section of Gastroenterology and Hepatology, Temple University School of Medicine, 3401 North Broad Street, Philadelphia, PA
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Jonigk D, Werlein C, Lee PD, Kauczor HU, Länger F, Ackermann M. Pulmonary and Systemic Pathology in COVID-19—Holistic Pathological Analyses. DEUTSCHES ARZTEBLATT INTERNATIONAL 2022; 119:429-435. [PMID: 35698804 PMCID: PMC9549895 DOI: 10.3238/arztebl.m2022.0231] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 03/22/2022] [Accepted: 05/10/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND The COVID-19 pandemic is the third worldwide coronavirus-associated disease outbreak in the past 20 years. Lung involvement, with acute respiratory distress syndrome (ARDS) in severe cases, is the main clinical feature of this disease; the cardiovascular system, the central nervous system, and the gastrointestinal tract can also be affected. The pathophysiology of both pulmonary and extrapulmonary organ damage was almost completely unknown when the pandemic began. METHODS This review is based on pertinent publications retrieved by a selective search concerning the structural changes and pathophysiology of COVID-19, with a focus on imaging techniques. RESULTS Immunohistochemical, electron-microscopic and molecular pathological analyses of tissues obtained by autopsy have improved our understanding of COVID-19 pathophysiology, including molecular regulatory mechanisms. Intussusceptive angiogenesis (IA) has been found to be a prominent pattern of damage in the affected organs of COVID-19 patients. In IA, an existing vessel changes by invagination of the endothelium and formation of an intraluminal septum, ultimately giving rise to two new lumina. This alters hemodynamics within the vessel, leading to a loss of laminar flow and its replacement by turbulent, inhomogeneous flow. IA, which arises because of ischemia due to thrombosis, is itself a risk factor for the generation of further microthrombi; these have been detected in the lungs, heart, liver, kidneys, brain, and placenta of COVID-19 patients. CONCLUSION Studies of autopsy material from various tissues of COVID-19 patients have revealed ultrastructural evidence of altered microvascularity, IA, and multifocal thrombi. These changes may contribute to the pathophysiology of post-acute interstitial fibrotic organ changes as well as to the clinical picture of long COVID.
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Affiliation(s)
- Danny Jonigk
- Institute of Pathology, Hannover Medical School, Hannover, Germany; German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover site, Hannover, Germany; Department of Mechanical Engineering, Faculty of Engineering Science, University College London, London, UK; Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany; Translational Lung Research Center Heidelberg, Heidelberg University Hospital, Heidelberg, Germany; Institute of Pathology and Molecular Pathology, Helios University Hospital Wuppertal, University Hospital of Witten-Herdecke, Wuppertal, Germany; Institute of Functional and Clinical Anatomy, University Medical Center Mainz, Mainz, Germany
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Furtado A, da Purificação CAC, Badaró R, Nascimento EGS. A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia. Diagnostics (Basel) 2022; 12:1527. [PMID: 35885433 PMCID: PMC9319098 DOI: 10.3390/diagnostics12071527] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 06/20/2022] [Accepted: 06/20/2022] [Indexed: 11/24/2022] Open
Abstract
A large number of reports present artificial intelligence (AI) algorithms, which support pneumonia detection caused by COVID-19 from chest CT (computed tomography) scans. Only a few studies provided access to the source code, which limits the analysis of the out-of-distribution generalization ability. This study presents Cimatec-CovNet-19, a new light 3D convolutional neural network inspired by the VGG16 architecture that supports COVID-19 identification from chest CT scans. We trained the algorithm with a dataset of 3000 CT Scans (1500 COVID-19-positive) with images from different parts of the world, enhanced with 3000 images obtained with data augmentation techniques. We introduced a novel pre-processing approach to perform a slice-wise selection based solely on the lung CT masks and an empirically chosen threshold for the very first slice. It required only 16 slices from a CT examination to identify COVID-19. The model achieved a recall of 0.88, specificity of 0.88, ROC-AUC of 0.95, PR-AUC of 0.95, and F1-score of 0.88 on a test set with 414 samples (207 COVID-19). These results support Cimatec-CovNet-19 as a good and light screening tool for COVID-19 patients. The whole code is freely available for the scientific community.
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Affiliation(s)
- Adhvan Furtado
- Supercomputing Center SENAI CIMATEC, Av. Orlando Gomes, 1845, Piatã, Salvador 41560-010, Brazil; (A.F.); (C.A.C.d.P.)
| | | | - Roberto Badaró
- Instituto SENAI de Inovação em Saúde, Av. Orlando Gomes, 1845, Piatã, Salvador 41560-010, Brazil;
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Imaging strategies used in emergency departments for the diagnostic workup of COVID-19 patients during the first wave of the pandemic: a multicenter retrospective cost-effectiveness analysis. Clin Microbiol Infect 2022; 28:1651.e1-1651.e8. [PMID: 35738321 PMCID: PMC9212395 DOI: 10.1016/j.cmi.2022.05.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/24/2022] [Accepted: 05/30/2022] [Indexed: 11/21/2022]
Abstract
OBJECTIVE Emergency departments (EDs) were on the front line for the diagnostic workup of patients with COVID-19 like symptoms during the first wave. Chest imaging was the key to rapidly identifying COVID-19 before administering RT-PCR which was time-consuming. The objective of our study was to compare the costs and organizational benefits of triage strategies in ED during the first wave of the COVID-19 pandemic. MATERIAL AND METHODS We conducted a retrospective study in five EDs in France, involving 3 712 consecutive patients consulting with COVID-like symptoms between March 9, 2020 and April 8, 2020, to assess the cost effectiveness of imaging strategies (chest radiography, chest CT scan in the presence of respiratory symptoms, systematic ultra-low dose (ULD) chest CT, and no systematic imaging) on ED length of stay (LOS) in the ED and on hospital costs. The ICER was calculated as the difference in costs divided by the difference in LOS. RESULTS Compared with chest radiography, workup with systematic ULD chest CT was the more cost-effective strategy (average LOS of 6.89 hours; average cost of €3 646), allowing for an almost 4-hour decrease in LOS in the ED at a cost increase of €98 per patient. Chest radiography (extendedly dominated) and rapid reverse-transcriptase polymerase chain reaction with no systematic imaging were the least effective strategies, with an average LOS of 10.8 hours. The strategy of chest CT in the presence of respiratory symptoms was more effective than the systematic ULD chest CT strategy, with the former providing a gain of 37 minutes at an extra cost of €718. CONCLUSION Systematic ULD chest CT for patients with COVID-like symptoms in the ED is a cost-effective strategy and should be considered to improve the management of patients in the ED during the pandemic, given the need to triage patients.
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The Role of Lung Ultrasound Monitoring in Early Detection of Ventilator-Associated Pneumonia in COVID-19 Patients: A Retrospective Observational Study. J Clin Med 2022; 11:jcm11113001. [PMID: 35683392 PMCID: PMC9181291 DOI: 10.3390/jcm11113001] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/11/2022] [Accepted: 05/21/2022] [Indexed: 02/08/2023] Open
Abstract
Specific lung ultrasound signs combined with clinical parameters allow for early diagnosis of ventilator-associated pneumonia in the general ICU population. This retrospective cohort study aimed to determine the accuracy of lung ultrasound monitoring for ventilator-associated pneumonia diagnosis in COVID-19 patients. Clinical (i.e., clinical pulmonary infection score) and ultrasound (i.e., presence of consolidation and a dynamic linear−arborescent air bronchogram, lung ultrasound score, ventilator-associated lung ultrasound score) data were collected on the day of the microbiological sample (pneumonia-day) and 48 h before (baseline) on 55 bronchoalveolar lavages of 33 mechanically-ventilated COVID-19 patients who were monitored daily with lung ultrasounds. A total of 26 samples in 23 patients were positive for ventilator-associated pneumonia (pneumonia cases). The onset of a dynamic linear−arborescent air bronchogram was 100% specific for ventilator-associated pneumonia. The ventilator-associated lung ultrasound score was higher in pneumonia-cases (2.5 (IQR 1.0 to 4.0) vs. 1.0 (IQR 1.0 to 1.0); p < 0.001); the lung ultrasound score increased from baseline in pneumonia-cases only (3.5 (IQR 2.0 to 6.0) vs. −1.0 (IQR −2.0 to 1.0); p = 0.0001). The area under the curve for clinical parameters, ventilator-associated pneumonia lung ultrasound score, and lung ultrasound score variations were 0.472, 0.716, and 0.800, respectively. A newly appeared dynamic linear−arborescent air bronchogram is highly specific for ventilator-associated pneumonia in COVID-19 patients. A high ventilator-associated pneumonia lung ultrasound score (or an increase in the lung ultrasound score) orients to ventilator-associated pneumonia.
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Shim SR, Kim SJ, Hong M, Lee J, Kang MG, Han HW. Diagnostic Performance of Antigen Rapid Diagnostic Tests, Chest Computed Tomography, and Lung Point-of-Care-Ultrasonography for SARS-CoV-2 Compared with RT-PCR Testing: A Systematic Review and Network Meta-Analysis. Diagnostics (Basel) 2022; 12:1302. [PMID: 35741112 PMCID: PMC9222155 DOI: 10.3390/diagnostics12061302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/04/2022] [Accepted: 05/20/2022] [Indexed: 12/10/2022] Open
Abstract
(1) Background: The comparative performance of various diagnostic methods for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection remains unclear. This study aimed to investigate the comparison of the 3 index test performances of rapid antigen diagnostic tests (RDTs), chest computed tomography (CT), and lung point-of-care-ultrasonography (US) with reverse transcription-polymerase chain reaction (RT-PCR), the reference standard, to provide more evidence-based data on the appropriate use of these index tests. (2) Methods: We retrieved data from electronic literature searches of PubMed, Cochrane Library, and EMBASE from 1 January 2020, to 1 April 2021. Diagnostic performance was examined using bivariate random-effects diagnostic test accuracy (DTA) and Bayesian network meta-analysis (NMA) models. (3) Results: Of the 3992 studies identified in our search, 118 including 69,445 participants met our selection criteria. Among these, 69 RDT, 38 CT, and 15 US studies in the pairwise meta-analysis were included for DTA with NMA. CT and US had high sensitivity of 0.852 (95% credible interval (CrI), 0.791-0.914) and 0.879 (95% CrI, 0.784-0.973), respectively. RDT had high specificity, 0.978 (95% CrI, 0.960-0.996). In accuracy assessment, RDT and CT had a relatively higher than US. However, there was no significant difference in accuracy between the 3 index tests. (4) Conclusions: This meta-analysis suggests that, compared with the reference standard RT-PCR, the 3 index tests (RDTs, chest CT, and lung US) had similar and complementary performances for diagnosis of SARS-CoV-2 infection. To manage and control COVID-19 effectively, future large-scale prospective studies could be used to obtain an optimal timely diagnostic process that identifies the condition of the patient accurately.
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Affiliation(s)
- Sung Ryul Shim
- Department of Health and Medical Informatics, Kyungnam University College of Health Sciences, Changwon 51767, Korea;
| | - Seong-Jang Kim
- Department of Nuclear Medicine, Pusan National University Yangsan Hospital, Yangsan 50615, Korea;
- Department of Nuclear Medicine, College of Medicine, Pusan National University, Yangsan 50615, Korea
- BioMedical Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50615, Korea
| | - Myunghee Hong
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea;
- Institute for Biomedical Informatics, School of Medicine, CHA University, Seongnam 13488, Korea
| | - Jonghoo Lee
- Department of Internal Medicine, Jeju National University Hospital, Jeju National University School of Medicine, Jeju 63241, Korea;
| | - Min-Gyu Kang
- Department of Internal Medicine, Chungbuk National University College of Medicine, Chungbuk National University Hospital, Cheongju 28644, Korea;
| | - Hyun Wook Han
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea;
- Institute for Biomedical Informatics, School of Medicine, CHA University, Seongnam 13488, Korea
- Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam 13488, Korea
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Gillissen A, Zimmermann T, Clasen S. Large pneumatocele as a rare complication in SARS-CoV-2 infection of the lung. Pneumologie 2022; 76:629-632. [PMID: 35504298 DOI: 10.1055/a-1771-5345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
In this paper, we present a case of SARS-CoV2-Virus a non-vaccinated 54-year-old male admitted with COVID-19 pneumonia and respiratory insufficiency requiring high-flow oxygen supplementation. CT-scan of the lung revealed multifocal bilateral ground-glass opacities and - as a rare complication - a large pneumatocele in the middle of the posterior part of the left lower lobe. In order to treat the pneumatocele, a 10 F was placed into the cavity. The resulting pneumothorax was successfully treated with a 20 F chest tube over a 9-day period. The pneumatocele shrank only slightly. This case demonstrates a unique radiologic finding in COVID-19, which is likely the result of severe inflammation secondary to SARS-CoV-2 including an unfruitful attempt at depressurisation.
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Affiliation(s)
- Adrian Gillissen
- Medizinische Klinik 3 (Pulmonary Medicine), Kreiskliniken Reutlingen GmbH, Reutlingen, Germany
| | - Thomas Zimmermann
- Klinik für Allgemein-, Viszeral- und Thoraxchirurgie, Kreiskliniken Reutlingen GmbH, Reutlingen, Germany
| | - Stephan Clasen
- Institut für Diagnostische und Interventionelle Radiologie, Kreisklinken Reutlingen GmbH, Reutlingen, Germany
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De Rosa L, L'Abbate S, Kusmic C, Faita F. Applications of artificial intelligence in lung ultrasound: Review of deep learning methods for COVID-19 fighting. Artif Intell Med Imaging 2022; 3:42-54. [DOI: 10.35711/aimi.v3.i2.42] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 02/22/2022] [Accepted: 04/26/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The pandemic outbreak of the novel coronavirus disease (COVID-19) has highlighted the need to combine rapid, non-invasive and widely accessible techniques with the least risk of patient’s cross-infection to achieve a successful early detection and surveillance of the disease. In this regard, the lung ultrasound (LUS) technique has been proved invaluable in both the differential diagnosis and the follow-up of COVID-19 patients, and its potential may be destined to evolve. Recently, indeed, LUS has been empowered through the development of automated image processing techniques.
AIM To provide a systematic review of the application of artificial intelligence (AI) technology in medical LUS analysis of COVID-19 patients using the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines.
METHODS A literature search was performed for relevant studies published from March 2020 - outbreak of the pandemic - to 30 September 2021. Seventeen articles were included in the result synthesis of this paper.
RESULTS As part of the review, we presented the main characteristics related to AI techniques, in particular deep learning (DL), adopted in the selected articles. A survey was carried out on the type of architectures used, availability of the source code, network weights and open access datasets, use of data augmentation, use of the transfer learning strategy, type of input data and training/test datasets, and explainability.
CONCLUSION Finally, this review highlighted the existing challenges, including the lack of large datasets of reliable COVID-19-based LUS images to test the effectiveness of DL methods and the ethical/regulatory issues associated with the adoption of automated systems in real clinical scenarios.
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Affiliation(s)
- Laura De Rosa
- Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Pisa 56124, Italy
| | - Serena L'Abbate
- Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Pisa 56124, Italy
- Institute of Life Sciences, Scuola Superiore Sant’Anna, Pisa 56124, Italy
| | - Claudia Kusmic
- Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Pisa 56124, Italy
| | - Francesco Faita
- Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Pisa 56124, Italy
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Leveraging deep learning for COVID-19 diagnosis through chest imaging. Neural Comput Appl 2022; 34:14003-14012. [PMID: 35462631 PMCID: PMC9017721 DOI: 10.1007/s00521-022-07250-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 03/29/2022] [Indexed: 11/30/2022]
Abstract
COVID-19 has taken a toll on the entire world, rendering serious illness and high mortality rate. In the present day, when the globe is hit by a pandemic, those suspected to be infected by the virus need to confirm its presence to seek immediate medical attention to avoid adverse outcomes and also to prevent further transmission of the virus in their close contacts by ensuring timely isolation. The most reliable laboratory testing currently available is the reverse transcription–polymerase chain reaction (RT-PCR) test. Although the test is considered gold standard, 20–25% of results can still be false negatives, which has lately led physicians to recommend medical imaging in specific cases. Our research examines the aspect of chest imaging as a method to diagnose COVID-19. This work is not directed to establish an alternative to RT-PCR, but to aid physicians in determining the presence of virus in medical images. As the disease presents lung involvement, it provides a basis to explore computer vision for classification in radiographic images. In this paper, authors compare the performance of various models, namely ResNet-50, EfficientNetB0, VGG-16 and a custom convolutional neural network (CNN) for detecting the presence of virus in chest computed tomography (CT) scan and chest X-ray images. The most promising results have been derived by using ResNet-50 on CT scans with an accuracy of 98.9% and ResNet-50 on X-rays with an accuracy of 98.7%, which offer an opportunity to further explore these methods for prospective use.
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Deep Learning Applied to Chest Radiograph Classification—A COVID-19 Pneumonia Experience. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083712] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Due to the recent COVID-19 pandemic, a large number of reports present deep learning algorithms that support the detection of pneumonia caused by COVID-19 in chest radiographs. Few studies have provided the complete source code, limiting testing and reproducibility on different datasets. This work presents Cimatec_XCOV19, a novel deep learning system inspired by the Inception-V3 architecture that is able to (i) support the identification of abnormal chest radiographs and (ii) classify the abnormal radiographs as suggestive of COVID-19. The training dataset has 44,031 images with 2917 COVID-19 cases, one of the largest datasets in recent literature. We organized and published an external validation dataset of 1158 chest radiographs from a Brazilian hospital. Two experienced radiologists independently evaluated the radiographs. The Cimatec_XCOV19 algorithm obtained a sensitivity of 0.85, specificity of 0.82, and AUC ROC of 0.93. We compared the AUC ROC of our algorithm with a well-known public solution and did not find a statistically relevant difference between both performances. We provide full access to the code and the test dataset, enabling this work to be used as a tool for supporting the fast screening of COVID-19 on chest X-ray exams, serving as a reference for educators, and supporting further algorithm enhancements.
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Lorent N, Vande Weygaerde Y, Claeys E, Guler Caamano Fajardo I, De Vos N, De Wever W, Salhi B, Gyselinck I, Bosteels C, Lambrecht BN, Everaerts S, Verschraegen S, Schepers C, Demeyer H, Heyns A, Depuydt P, Oeyen S, Van Bleyenbergh P, Godinas L, Dupont L, Hermans G, Derom E, Gosselink R, Janssens W, Van Braeckel E. Prospective longitudinal evaluation of hospitalised COVID-19 survivors 3 and 12 months after discharge. ERJ Open Res 2022; 8:00004-2022. [PMID: 35415186 PMCID: PMC8994962 DOI: 10.1183/23120541.00004-2022] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 03/04/2022] [Indexed: 12/20/2022] Open
Abstract
Background Long-term outcome data of coronavirus disease 2019 (COVID-19) survivors are needed to understand their recovery trajectory and additional care needs. Methods A prospective observational multicentre cohort study was carried out of adults hospitalised with COVID-19 from March through May 2020. Workup at 3 and 12 months following admission consisted of clinical review, pulmonary function testing, 6-min walk distance (6MWD), muscle strength, chest computed tomography (CT) and quality of life questionnaires. We evaluated factors correlating with recovery by linear mixed effects modelling. Results Of 695 patients admitted, 299 and 226 returned at 3 and 12 months, respectively (median age 59 years, 69% male, 31% severe disease). About half and a third of the patients reported fatigue, dyspnoea and/or cognitive impairment at 3 and 12 months, respectively. Reduced 6MWD and quadriceps strength were present in 20% and 60% at 3 months versus 7% and 30% at 12 months. A high anxiety score and body mass index correlated with poor functional recovery. At 3 months, diffusing capacity for carbon monoxide (DLCO) and total lung capacity were below the lower limit of normal in 35% and 18%, decreasing to 21% and 16% at 12 months; predictors of poor DLCO recovery were female sex, pre-existing lung disease, smoking and disease severity. Chest CT improved over time; 10% presented non-progressive fibrotic changes at 1 year. Conclusion Many COVID-19 survivors, especially those with severe disease, experienced limitations at 3 months. At 1 year, the majority showed improvement to almost complete recovery. To identify additional care or rehabilitation needs, we recommend a timely multidisciplinary follow-up visit following COVID-19 admission. Most hospitalised #COVID19 survivors show promising recovery 1 year after discharge, although mild symptoms may linger. Severe impairments are rare, but this study suggests an evaluation of the individual care needs after discharge.https://bit.ly/3sZK45x
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Canan MGM, Sokoloski CS, Dias VL, Andrade JMCD, Basso ACN, Chomiski C, Escuissato DL, Andrade Junior IC, Vaz IC, Stival RSM, Storrer KM. Chest CT as a Prognostic Tool in COVID-19. Arch Bronconeumol 2022; 58:69-72. [PMID: 35431085 PMCID: PMC8895706 DOI: 10.1016/j.arbres.2022.02.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 02/24/2022] [Accepted: 02/25/2022] [Indexed: 01/19/2023]
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Kawooya MG, Kisembo HN, Remedios D, Malumba R, del Rosario Perez M, Ige T, Hasford F, Brown JK, Lette MM, Mansouri B, Salama DH, Peer F, Nyabanda R. An Africa point of view on quality and safety in imaging. Insights Imaging 2022; 13:58. [PMID: 35347470 PMCID: PMC8959275 DOI: 10.1186/s13244-022-01203-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/26/2022] [Indexed: 11/16/2022] Open
Abstract
Africa has seen an upsurge in diagnostic imaging utilization, with benefits of efficient and accurate diagnosis, but these could easily be offset by undesirable effects attributed to unjustified, unoptimized imaging and poor quality examinations. This paper aims to present Africa’s position regarding quality and safety in imaging, give reasons for the rising interest in quality and safety, define quality and safety from an African context, list drivers for quality and safety in Africa, discuss the impact of COVID-19 on quality and safety, and review Africa’s progress using the Bonn Call for Action framework while proposing a way forward for imaging quality and safety in Africa. In spite of a healthcare setting characterized by meagre financial, human and technology resources, a rapidly widening disease-burden spectrum, growing proportion of non-communicable diseases and resurgence of tropical and global infections, Africa has over the last ten years made significant strides in quality and safety for imaging. These include raising radiation-safety awareness, interest and application of evidence-based radiation safety recommendations and guidance tools, establishing facility and national diagnostic reference levels (DRLs) and strengthening end-user education and training. Major challenges are: limited human resource, low prioritization of imaging in relation to other health services, low level of integration of imaging into the entire health service delivery, insufficient awareness for radiation safety awareness, a radiation safety culture which is emerging, insufficient facilities and opportunities for education and training. Solutions to these challenges should target the entire hierarchy of health service delivery from prioritization, policy, planning, processes to procedures.
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Gillman AG, Lunardo F, Prinable J, Belous G, Nicolson A, Min H, Terhorst A, Dowling JA. Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review. Phys Eng Sci Med 2022; 45:13-29. [PMID: 34919204 PMCID: PMC8678975 DOI: 10.1007/s13246-021-01093-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 12/13/2021] [Indexed: 12/31/2022]
Abstract
OBJECTIVES To conduct a systematic survey of published techniques for automated diagnosis and prognosis of COVID-19 diseases using medical imaging, assessing the validity of reported performance and investigating the proposed clinical use-case. To conduct a scoping review into the authors publishing such work. METHODS The Scopus database was queried and studies were screened for article type, and minimum source normalized impact per paper and citations, before manual relevance assessment and a bias assessment derived from a subset of the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). The number of failures of the full CLAIM was adopted as a surrogate for risk-of-bias. Methodological and performance measurements were collected from each technique. Each study was assessed by one author. Comparisons were evaluated for significance with a two-sided independent t-test. FINDINGS Of 1002 studies identified, 390 remained after screening and 81 after relevance and bias exclusion. The ratio of exclusion for bias was 71%, indicative of a high level of bias in the field. The mean number of CLAIM failures per study was 8.3 ± 3.9 [1,17] (mean ± standard deviation [min,max]). 58% of methods performed diagnosis versus 31% prognosis. Of the diagnostic methods, 38% differentiated COVID-19 from healthy controls. For diagnostic techniques, area under the receiver operating curve (AUC) = 0.924 ± 0.074 [0.810,0.991] and accuracy = 91.7% ± 6.4 [79.0,99.0]. For prognostic techniques, AUC = 0.836 ± 0.126 [0.605,0.980] and accuracy = 78.4% ± 9.4 [62.5,98.0]. CLAIM failures did not correlate with performance, providing confidence that the highest results were not driven by biased papers. Deep learning techniques reported higher AUC (p < 0.05) and accuracy (p < 0.05), but no difference in CLAIM failures was identified. INTERPRETATION A majority of papers focus on the less clinically impactful diagnosis task, contrasted with prognosis, with a significant portion performing a clinically unnecessary task of differentiating COVID-19 from healthy. Authors should consider the clinical scenario in which their work would be deployed when developing techniques. Nevertheless, studies report superb performance in a potentially impactful application. Future work is warranted in translating techniques into clinical tools.
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Affiliation(s)
- Ashley G Gillman
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia.
| | - Febrio Lunardo
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
- College of Science and Engineering, James Cook University, Australian Tropical Science Innovation Precinct, Townsville, QLD, 4814, Australia
| | - Joseph Prinable
- ACRF Image X Institute, University of Sydney, Level 2, Biomedical Building (C81), 1 Central Ave, Australian Technology Park, Eveleigh, Sydney, NSW, 2015, Australia
| | - Gregg Belous
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
| | - Aaron Nicolson
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
| | - Hang Min
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
| | - Andrew Terhorst
- Data61, Commonwealth Scientific and Industrial Research Organisation, College Road, Sandy Bay, Hobart, TAS, 7005, Australia
| | - Jason A Dowling
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
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Tricarico D, Calandri M, Barba M, Piatti C, Geninatti C, Basile D, Gatti M, Melis M, Veltri A. Convolutional Neural Network-Based Automatic Analysis of Chest Radiographs for the Detection of COVID-19 Pneumonia: A Prioritizing Tool in the Emergency Department, Phase I Study and Preliminary "Real Life" Results. Diagnostics (Basel) 2022; 12:570. [PMID: 35328122 PMCID: PMC8947382 DOI: 10.3390/diagnostics12030570] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 02/11/2022] [Accepted: 02/15/2022] [Indexed: 12/26/2022] Open
Abstract
The aim of our study is the development of an automatic tool for the prioritization of COVID-19 diagnostic workflow in the emergency department by analyzing chest X-rays (CXRs). The Convolutional Neural Network (CNN)-based method we propose has been tested retrospectively on a single-center set of 542 CXRs evaluated by experienced radiologists. The SARS-CoV-2 positive dataset (n = 234) consists of CXRs collected between March and April 2020, with the COVID-19 infection being confirmed by an RT-PCR test within 24 h. The SARS-CoV-2 negative dataset (n = 308) includes CXRs from 2019, therefore prior to the pandemic. For each image, the CNN computes COVID-19 risk indicators, identifying COVID-19 cases and prioritizing the urgent ones. After installing the software into the hospital RIS, a preliminary comparison between local daily COVID-19 cases and predicted risk indicators for 2918 CXRs in the same period was performed. Significant improvements were obtained for both prioritization and identification using the proposed method. Mean Average Precision (MAP) increased (p < 1.21 × 10−21 from 43.79% with random sorting to 71.75% with our method. CNN sensitivity was 78.23%, higher than radiologists’ 61.1%; specificity was 64.20%. In the real-life setting, this method had a correlation of 0.873. The proposed CNN-based system effectively prioritizes CXRs according to COVID-19 risk in an experimental setting; preliminary real-life results revealed high concordance with local pandemic incidence.
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Affiliation(s)
- Davide Tricarico
- AITEM Artificial Intelligence Technologies Multipurpose s.r.l., Corso Castelfidardo 36, 10129 Turin, Italy; (D.T.); (M.M.)
- Department of Mathematics “G. Peano”, University of Turin, Via Carlo Alberto 10, 10123 Turin, Italy
| | - Marco Calandri
- Diagnostic and Interventional Radiology Unit, Oncology Department, San Luigi Gonzaga University Hospital, University of Turin, Regione Gonzole 10, 10043 Orbassano, Turin, Italy; (M.C.); (C.P.); (C.G.); (D.B.); (A.V.)
| | - Matteo Barba
- Diagnostic and Interventional Radiology Unit, Oncology Department, San Luigi Gonzaga University Hospital, University of Turin, Regione Gonzole 10, 10043 Orbassano, Turin, Italy; (M.C.); (C.P.); (C.G.); (D.B.); (A.V.)
| | - Clara Piatti
- Diagnostic and Interventional Radiology Unit, Oncology Department, San Luigi Gonzaga University Hospital, University of Turin, Regione Gonzole 10, 10043 Orbassano, Turin, Italy; (M.C.); (C.P.); (C.G.); (D.B.); (A.V.)
| | - Carlotta Geninatti
- Diagnostic and Interventional Radiology Unit, Oncology Department, San Luigi Gonzaga University Hospital, University of Turin, Regione Gonzole 10, 10043 Orbassano, Turin, Italy; (M.C.); (C.P.); (C.G.); (D.B.); (A.V.)
| | - Domenico Basile
- Diagnostic and Interventional Radiology Unit, Oncology Department, San Luigi Gonzaga University Hospital, University of Turin, Regione Gonzole 10, 10043 Orbassano, Turin, Italy; (M.C.); (C.P.); (C.G.); (D.B.); (A.V.)
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, Città della Salute e della Scienza di Torino, Corso Bramante, 88/90, 10126 Turin, Italy;
| | - Massimiliano Melis
- AITEM Artificial Intelligence Technologies Multipurpose s.r.l., Corso Castelfidardo 36, 10129 Turin, Italy; (D.T.); (M.M.)
| | - Andrea Veltri
- Diagnostic and Interventional Radiology Unit, Oncology Department, San Luigi Gonzaga University Hospital, University of Turin, Regione Gonzole 10, 10043 Orbassano, Turin, Italy; (M.C.); (C.P.); (C.G.); (D.B.); (A.V.)
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