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1
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Nathani P, Sharma P. Role of Artificial Intelligence in the Detection and Management of Premalignant and Malignant Lesions of the Esophagus and Stomach. Gastrointest Endosc Clin N Am 2025; 35:319-353. [PMID: 40021232 DOI: 10.1016/j.giec.2024.10.003] [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] [Indexed: 03/03/2025]
Abstract
The advent of artificial intelligence (AI) and deep learning algorithms, particularly convolutional neural networks, promises to address pitfalls, bridging the care for patients at high risk with improved detection (computer-aided detection [CADe]) and characterization (computer-aided diagnosis [CADx]) of lesions. This review describes the available artificial intelligence (AI) technology and the current data on AI tools for screening esophageal squamous cell cancer, Barret's esophagus-related neoplasia, and gastric cancer. These tools outperformed endoscopists in many situations. Recent randomized controlled trials have demonstrated the successful application of AI tools in clinical practice with improved outcomes.
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Affiliation(s)
- Piyush Nathani
- Department of Gastroenterology, University of Kansas School of Medicine, Kansas City, KS, USA.
| | - Prateek Sharma
- Department of Gastroenterology, University of Kansas School of Medicine, Kansas City, KS, USA; Kansas City Veteran Affairs Medical Center, Kansas City, MO, USA
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2
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Kusters CHJ, Jaspers TJM, Boers TGW, Jong MR, Jukema JB, Fockens KN, de Groof AJ, Bergman JJ, van der Sommen F, De With PHN. Will Transformers change gastrointestinal endoscopic image analysis? A comparative analysis between CNNs and Transformers, in terms of performance, robustness and generalization. Med Image Anal 2025; 99:103348. [PMID: 39298861 DOI: 10.1016/j.media.2024.103348] [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: 06/09/2023] [Revised: 07/10/2024] [Accepted: 09/10/2024] [Indexed: 09/22/2024]
Abstract
Gastrointestinal endoscopic image analysis presents significant challenges, such as considerable variations in quality due to the challenging in-body imaging environment, the often-subtle nature of abnormalities with low interobserver agreement, and the need for real-time processing. These challenges pose strong requirements on the performance, generalization, robustness and complexity of deep learning-based techniques in such safety-critical applications. While Convolutional Neural Networks (CNNs) have been the go-to architecture for endoscopic image analysis, recent successes of the Transformer architecture in computer vision raise the possibility to update this conclusion. To this end, we evaluate and compare clinically relevant performance, generalization and robustness of state-of-the-art CNNs and Transformers for neoplasia detection in Barrett's esophagus. We have trained and validated several top-performing CNNs and Transformers on a total of 10,208 images (2,079 patients), and tested on a total of 7,118 images (998 patients) across multiple test sets, including a high-quality test set, two internal and two external generalization test sets, and a robustness test set. Furthermore, to expand the scope of the study, we have conducted the performance and robustness comparisons for colonic polyp segmentation (Kvasir-SEG) and angiodysplasia detection (Giana). The results obtained for featured models across a wide range of training set sizes demonstrate that Transformers achieve comparable performance as CNNs on various applications, show comparable or slightly improved generalization capabilities and offer equally strong resilience and robustness against common image corruptions and perturbations. These findings confirm the viability of the Transformer architecture, particularly suited to the dynamic nature of endoscopic video analysis, characterized by fluctuating image quality, appearance and equipment configurations in transition from hospital to hospital. The code is made publicly available at: https://github.com/BONS-AI-VCA-AMC/Endoscopy-CNNs-vs-Transformers.
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Affiliation(s)
- Carolus H J Kusters
- Department of Electrical Engineering, Video Coding & Architectures, Eindhoven University of Technology, Eindhoven, The Netherlands.
| | - Tim J M Jaspers
- Department of Electrical Engineering, Video Coding & Architectures, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Tim G W Boers
- Department of Electrical Engineering, Video Coding & Architectures, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Martijn R Jong
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jelmer B Jukema
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Kiki N Fockens
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Albert J de Groof
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jacques J Bergman
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Video Coding & Architectures, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Peter H N De With
- Department of Electrical Engineering, Video Coding & Architectures, Eindhoven University of Technology, Eindhoven, The Netherlands
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3
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Antonelli G, Libanio D, De Groof AJ, van der Sommen F, Mascagni P, Sinonquel P, Abdelrahim M, Ahmad O, Berzin T, Bhandari P, Bretthauer M, Coimbra M, Dekker E, Ebigbo A, Eelbode T, Frazzoni L, Gross SA, Ishihara R, Kaminski MF, Messmann H, Mori Y, Padoy N, Parasa S, Pilonis ND, Renna F, Repici A, Simsek C, Spadaccini M, Bisschops R, Bergman JJGHM, Hassan C, Dinis Ribeiro M. QUAIDE - Quality assessment of AI preclinical studies in diagnostic endoscopy. Gut 2024; 74:153-161. [PMID: 39406471 DOI: 10.1136/gutjnl-2024-332820] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 09/27/2024] [Indexed: 12/12/2024]
Abstract
Artificial intelligence (AI) holds significant potential for enhancing quality of gastrointestinal (GI) endoscopy, but the adoption of AI in clinical practice is hampered by the lack of rigorous standardisation and development methodology ensuring generalisability. The aim of the Quality Assessment of pre-clinical AI studies in Diagnostic Endoscopy (QUAIDE) Explanation and Checklist was to develop recommendations for standardised design and reporting of preclinical AI studies in GI endoscopy.The recommendations were developed based on a formal consensus approach with an international multidisciplinary panel of 32 experts among endoscopists and computer scientists. The Delphi methodology was employed to achieve consensus on statements, with a predetermined threshold of 80% agreement. A maximum three rounds of voting were permitted.Consensus was reached on 18 key recommendations, covering 6 key domains: data acquisition and annotation (6 statements), outcome reporting (3 statements), experimental setup and algorithm architecture (4 statements) and result presentation and interpretation (5 statements). QUAIDE provides recommendations on how to properly design (1. Methods, statements 1-14), present results (2. Results, statements 15-16) and integrate and interpret the obtained results (3. Discussion, statements 17-18).The QUAIDE framework offers practical guidance for authors, readers, editors and reviewers involved in AI preclinical studies in GI endoscopy, aiming at improving design and reporting, thereby promoting research standardisation and accelerating the translation of AI innovations into clinical practice.
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Affiliation(s)
- Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli, Ariccia, Rome, Italy
| | - Diogo Libanio
- MEDCIDS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Albert Jeroen De Groof
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, VCA group, University of Technology, Eindhoven, The Netherlands
| | - Pietro Mascagni
- IHU Strasbourg, Strasbourg, France
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Pieter Sinonquel
- Department of Gastroenterology and Hepatology, UZ Leuven, Leuven, Belgium
- Department of Translational Research for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
| | | | | | - Tyler Berzin
- Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Pradeep Bhandari
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | | | - Miguel Coimbra
- INESC TEC, Faculdade de Ciências, University of Porto, Porto, Portugal
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Alanna Ebigbo
- III Medizinische Klinik, UniversitatsKlinikum Augsburg, Augsburg, Germany
| | - Tom Eelbode
- Department of Electrical Engineering (ESAT/PSI), Medical Imaging Research Center, KU Leuven, Leuven, Belgium
| | - Leonardo Frazzoni
- Gastroenterology and Endoscopy Unit, Forlì-Cesena Hospitals, AUSL Romagna, Forlì, Italy
| | - Seth A Gross
- Division of Gastroenterology and Hepatology, New York University Langone Health, New York, New York, USA
| | - Ryu Ishihara
- Osaka International Cancer Institute, Osaka, Japan
| | - Michal Filip Kaminski
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Department of Gastroenterological Oncology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland
- Medical Center for Postgraduate Education, Warsaw, Poland
| | - Helmut Messmann
- III Medizinische Klinik, UniversitatsKlinikum Augsburg, Augsburg, Germany
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | | | | | - Nastazja Dagny Pilonis
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Department of Gastroenterological Oncology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland
- Medical Center for Postgraduate Education, Warsaw, Poland
| | - Francesco Renna
- INESC TEC, Faculdade de Ciências, University of Porto, Porto, Portugal
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy
| | - Cem Simsek
- Department of Gastroenterology, Hacettepe University, Ankara, Turkey
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, UZ Leuven, Leuven, Belgium
- Department of Translational Research for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
| | - Jacques J G H M Bergman
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy
| | - Mario Dinis Ribeiro
- MEDCIDS, Faculty of Medicine, University of Porto, Porto, Portugal
- RISE@CI-IPOP (Health Research Network), Porto Comprehensive Cancer Centre (Porto.CCC), Porto, Portugal
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Li S, Xu M, Meng Y, Sun H, Zhang T, Yang H, Li Y, Ma X. The application of the combination between artificial intelligence and endoscopy in gastrointestinal tumors. MEDCOMM – ONCOLOGY 2024; 3. [DOI: 10.1002/mog2.91] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 09/03/2024] [Indexed: 01/04/2025]
Abstract
AbstractGastrointestinal (GI) tumors have always been a major type of malignant tumor and a leading cause of tumor‐related deaths worldwide. The main principles of modern medicine for GI tumors are early prevention, early diagnosis, and early treatment, with early diagnosis being the most effective measure. Endoscopy, due to its ability to visualize lesions, has been one of the primary modalities for screening, diagnosing, and treating GI tumors. However, a qualified endoscopist often requires long training and extensive experience, which to some extent limits the wider use of endoscopy. With advances in data science, artificial intelligence (AI) has brought a new development direction for the endoscopy of GI tumors. AI can quickly process large quantities of data and images and improve diagnostic accuracy with some training, greatly reducing the workload of endoscopists and assisting them in early diagnosis. Therefore, this review focuses on the combined application of endoscopy and AI in GI tumors in recent years, describing the latest research progress on the main types of tumors and their performance in clinical trials, the application of multimodal AI in endoscopy, the development of endoscopy, and the potential applications of AI within it, with the aim of providing a reference for subsequent research.
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Affiliation(s)
- Shen Li
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Maosen Xu
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy, West China Hospital, National Clinical Research, Sichuan University Chengdu Sichuan China
| | - Yuanling Meng
- West China School of Stomatology Sichuan University Chengdu Sichuan China
| | - Haozhen Sun
- College of Life Sciences Sichuan University Chengdu Sichuan China
| | - Tao Zhang
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Hanle Yang
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Yueyi Li
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Xuelei Ma
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
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Sreedharan JK, Saleh F, Alqahtani A, Albalawi IA, Gopalakrishnan GK, Alahmed HA, Alsultan BA, Alalharith DM, Alnasser M, Alahmari AD, Karthika M. Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis. Front Artif Intell 2024; 7:1422551. [PMID: 39430618 PMCID: PMC11487586 DOI: 10.3389/frai.2024.1422551] [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: 04/24/2024] [Accepted: 09/23/2024] [Indexed: 10/22/2024] Open
Abstract
Introduction Artificial intelligence has come to be the highlight in almost all fields of science. It uses various models and algorithms to detect patterns and specific findings to diagnose a disease with utmost accuracy. With the increasing need for accurate and precise diagnosis of disease, employing artificial intelligence models and concepts in healthcare setup can be beneficial. Methodology The search engines and databases employed in this study are PubMed, ScienceDirect and Medline. Studies published between 1st January 2013 to 1st February 2023 were included in this analysis. The selected articles were screened preliminarily using the Rayyan web tool, after which investigators screened the selected articles individually. The risk of bias for the selected studies was assessed using QUADAS-2 tool specially designed to test bias among studies related to diagnostic test reviews. Results In this review, 17 studies were included from a total of 12,173 studies. These studies were analysed for their sensitivity, accuracy, positive predictive value, specificity and negative predictive value in diagnosing barrette's neoplasia, cardiac arrest, esophageal adenocarcinoma, sepsis and gastrointestinal stromal tumors. All the studies reported heterogeneity with p-value <0.05 at confidence interval 95%. Conclusion The existing evidential data suggests that artificial intelligence can be highly helpful in the field of diagnosis providing maximum precision and early detection. This helps to prevent disease progression and also helps to provide treatment at the earliest. Employing artificial intelligence in diagnosis will define the advancement of health care environment and also be beneficial in every aspect concerned with treatment to illnesses.
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Affiliation(s)
- Jithin K. Sreedharan
- Department of Respiratory Therapy, College of Health Sciences, University of Doha for Science and Technology, Doha, Qatar
| | - Fred Saleh
- Deanship—College of Health Sciences, University of Doha for Science and Technology, Doha, Qatar
| | - Abdullah Alqahtani
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | - Ibrahim Ahmed Albalawi
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | | | | | | | | | - Musallam Alnasser
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | - Ayedh Dafer Alahmari
- Department of Rehabilitation Science, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Manjush Karthika
- Faculty of Medical and Health Sciences, Liwa College, Abu Dhabi, United Arab Emirates
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Jong MR, de Groof AJ. Advancement of artificial intelligence systems for surveillance endoscopy of Barrett's esophagus. Dig Liver Dis 2024; 56:1126-1130. [PMID: 38071181 DOI: 10.1016/j.dld.2023.11.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 06/29/2024]
Abstract
Barrett's esophagus (BE) is a precursor disease for esophageal adenocarcinoma. Timely detection and treatment has significant influence on patient outcomes. Over the last years, several artificial intelligence (AI) systems have emerged to assist the endoscopist. The primary focus of research has been computer aided detection (CADe). Several groups have succeeded in developing competitive models for neoplasia detection. Additionally, computer aided diagnosis (CADx) models have been developed for subsequent lesion characterization and assistance in clinical decision making. Future studies should focus on bridging the domain gap between academic development and integration in daily practice.
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Affiliation(s)
- M R Jong
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - A J de Groof
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
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7
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Zhao SQ, Liu WT. Progress in artificial intelligence assisted digestive endoscopy diagnosis of digestive system diseases. WORLD CHINESE JOURNAL OF DIGESTOLOGY 2024; 32:171-181. [DOI: 10.11569/wcjd.v32.i3.171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2024]
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Elshaarawy O, Alboraie M, El-Kassas M. Artificial Intelligence in endoscopy: A future poll. Arab J Gastroenterol 2024; 25:13-17. [PMID: 38220477 DOI: 10.1016/j.ajg.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 09/18/2022] [Accepted: 11/28/2023] [Indexed: 01/16/2024]
Abstract
Artificial Intelligence [AI] has been a trendy topic in recent years, with many developed medical applications. In gastrointestinal endoscopy, AI systems include computer-assisted detection [CADe] for lesion detection as bleedings and polyps and computer-assisted diagnosis [CADx] for optical biopsy and lesion characterization. The technology behind these systems is based on a computer algorithm that is trained for a specific function. This function could be to recognize or characterize target lesions such as colonic polyps. Moreover, AI systems can offer technical assistance to improve endoscopic performance as scope insertion guidance. Currently, we believe that such technologies still lack legal and regulatory validations as a large sector of doctors and patients have concerns. However, there is no doubt that these technologies will bring significant improvement in the endoscopic management of patients as well as save money and time.
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Affiliation(s)
- Omar Elshaarawy
- Hepatology and Gastroenterology Department, National Liver Institute, Menoufia University, Menoufia, Egypt; Gastroenterology Department, Royal Liverpool University Hospital, NHS, UK
| | - Mohamed Alboraie
- Department of Internal Medicine, Al-Azhar University, Cairo, Egypt
| | - Mohamed El-Kassas
- Endemic Medicine Department, Faculty of Medicine, Helwan University, Cairo, Egypt.
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Chen H, Liu SY, Huang SH, Liu M, Chen GX. Applications of artificial intelligence in gastroscopy: a narrative review. J Int Med Res 2024; 52:3000605231223454. [PMID: 38235690 PMCID: PMC10798083 DOI: 10.1177/03000605231223454] [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: 09/20/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024] Open
Abstract
Gastroscopy, a critical tool for the diagnosis of upper gastrointestinal diseases, has recently incorporated artificial intelligence (AI) technology to alleviate the challenges involved in endoscopic diagnosis of some lesions, thereby enhancing diagnostic accuracy. This narrative review covers the current status of research concerning various applications of AI technology to gastroscopy, then discusses future research directions. By providing this review, we hope to promote the integration of gastroscopy and AI technology, with long-term clinical applications that can assist patients.
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Affiliation(s)
- Hu Chen
- The First Clinical Medical School, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Shi-yu Liu
- Department of Gastroenterology, Xuzhou Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Si-hui Huang
- Department of Gastroenterology, Xuzhou Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Min Liu
- School of Chemical Engineering & Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China
| | - Guang-xia Chen
- Department of Gastroenterology, Xuzhou Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, China
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Chempak Kumar A, Mubarak DMN. Ensembled CNN with artificial bee colony optimization method for esophageal cancer stage classification using SVM classifier. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:31-51. [PMID: 37980593 DOI: 10.3233/xst-230111] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
BACKGROUND Esophageal cancer (EC) is aggressive cancer with a high fatality rate and a rapid rise of the incidence globally. However, early diagnosis of EC remains a challenging task for clinicians. OBJECTIVE To help address and overcome this challenge, this study aims to develop and test a new computer-aided diagnosis (CAD) network that combines several machine learning models and optimization methods to detect EC and classify cancer stages. METHODS The study develops a new deep learning network for the classification of the various stages of EC and the premalignant stage, Barrett's Esophagus from endoscopic images. The proposed model uses a multi-convolution neural network (CNN) model combined with Xception, Mobilenetv2, GoogLeNet, and Darknet53 for feature extraction. The extracted features are blended and are then applied on to wrapper based Artificial Bee Colony (ABC) optimization technique to grade the most accurate and relevant attributes. A multi-class support vector machine (SVM) classifies the selected feature set into the various stages. A study dataset involving 523 Barrett's Esophagus images, 217 ESCC images and 288 EAC images is used to train the proposed network and test its classification performance. RESULTS The proposed network combining Xception, mobilenetv2, GoogLeNet, and Darknet53 outperforms all the existing methods with an overall classification accuracy of 97.76% using a 3-fold cross-validation method. CONCLUSION This study demonstrates that a new deep learning network that combines a multi-CNN model with ABC and a multi-SVM is more efficient than those with individual pre-trained networks for the EC analysis and stage classification.
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Affiliation(s)
- A Chempak Kumar
- Department of Computer Science, University of Kerala, Trivandrum, Kerala, India
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Ahn JC, Shah VH. Artificial intelligence in gastroenterology and hepatology. ARTIFICIAL INTELLIGENCE IN CLINICAL PRACTICE 2024:443-464. [DOI: 10.1016/b978-0-443-15688-5.00016-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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12
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Xia J, Jiang B, Pan J, Liao Z. Imaging of gastrointestinal endoscopy. TRANSPATHOLOGY 2024:171-183. [DOI: 10.1016/b978-0-323-95223-1.00026-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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Guidozzi N, Menon N, Chidambaram S, Markar SR. The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis. Dis Esophagus 2023; 36:doad048. [PMID: 37480192 PMCID: PMC10789250 DOI: 10.1093/dote/doad048] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma-the pooled sensitivity and specificity were 91.2% (84.3-95.2%) and 80% (64.3-89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8-96.4) and 86.9% (81.7-90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.
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Affiliation(s)
- Nadia Guidozzi
- Department of General Surgery, University of Witwatersrand, Johannesburg, South Africa
| | - Nainika Menon
- Department of General Surgery, Oxford University Hospitals, Oxford, UK
| | - Swathikan Chidambaram
- Academic Surgical Unit, Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital, London, UK
| | - Sheraz Rehan Markar
- Department of General Surgery, Oxford University Hospitals, Oxford, UK
- Nuffield Department of Surgery, University of Oxford, Oxford, UK
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14
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Patel A, Arora GS, Roknsharifi M, Kaur P, Javed H. Artificial Intelligence in the Detection of Barrett's Esophagus: A Systematic Review. Cureus 2023; 15:e47755. [PMID: 38021699 PMCID: PMC10676286 DOI: 10.7759/cureus.47755] [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] [Accepted: 10/26/2023] [Indexed: 12/01/2023] Open
Abstract
Barrett's esophagus (BE) remains a significant precursor to esophageal adenocarcinoma, requiring accurate and efficient diagnosis and management. The increasing application of machine learning (ML) technologies presents a transformative opportunity for diagnosing and treating BE. This systematic review evaluates the effectiveness and accuracy of machine learning technologies in BE diagnosis and management by conducting a comprehensive search across PubMed, Scopus, and Web of Science databases up to the year 2023. The studies were organized into five categories: computer-aided systems, natural language processing and text-based systems, deep learning on histology and biopsy images, real-time and video analysis, and miscellaneous studies. Results indicate high sensitivity and specificity across machine learning applications. Specifically, computer-aided systems showed sensitivities ranging from 84% to 100% and specificities from 64% to 90.7%. Natural language processing and text-based systems achieved an accuracy as high as 98.7%. Deep learning techniques applied to histology and biopsy images displayed sensitivities up to greater than 90% and a specificity of 100%. Furthermore, real-time and video analysis technologies demonstrated high performance with assessment speeds of up to 48 frames per second (fps) and a mean average precision of 75.3%. Overall, the reviewed literature underscores the growing capability and efficiency of machine learning technologies in diagnosing and managing Barrett's esophagus, often outperforming traditional diagnostic methods. These findings highlight the promising future role of machine learning in enhancing clinical practice and improving patient care for Barrett's esophagus.
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Affiliation(s)
- Akash Patel
- Internal Medicine, Eisenhower Health, Rancho Mirage, USA
| | - Gagandeep Singh Arora
- Hepatobiliary Pancreatic Surgery and Liver Transplant, BLK-Max Super Speciality Hospital, New Delhi, IND
- Internal Medicine, University of California, Riverside, San Bernardino, USA
| | - Mona Roknsharifi
- Internal Medicine, University of California, Riverside, San Bernardino, USA
| | - Parneet Kaur
- Emergency, Civil Hospital, Mukerian, IND
- Internal Medicine, Suburban Community Hospital, Philadelphia, USA
| | - Hamna Javed
- Internal Medicine, Saint Agnes Medical Center, Fresno, USA
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15
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Tee CHN, Ravi R, Ang TL, Li JW. Role of artificial intelligence in Barrett’s esophagus. Artif Intell Gastroenterol 2023; 4:28-35. [DOI: 10.35712/aig.v4.i2.28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/17/2023] [Accepted: 06/12/2023] [Indexed: 09/07/2023] Open
Abstract
The application of artificial intelligence (AI) in gastrointestinal endoscopy has gained significant traction over the last decade. One of the more recent applications of AI in this field includes the detection of dysplasia and cancer in Barrett’s esophagus (BE). AI using deep learning methods has shown promise as an adjunct to the endoscopist in detecting dysplasia and cancer. Apart from visual detection and diagnosis, AI may also aid in reducing the considerable interobserver variability in identifying and distinguishing dysplasia on whole slide images from digitized BE histology slides. This review aims to provide a comprehensive summary of the key studies thus far as well as providing an insight into the future role of AI in Barrett’s esophagus.
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Affiliation(s)
- Chin Hock Nicholas Tee
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - Rajesh Ravi
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
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16
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Ribolsi M, Savarino E. Towards a better diagnosis of gastro esophageal reflux disease. Expert Rev Gastroenterol Hepatol 2023; 17:999-1010. [PMID: 37800858 DOI: 10.1080/17474124.2023.2267435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 10/03/2023] [Indexed: 10/07/2023]
Abstract
INTRODUCTION GERD is a common disorder and is characterized by the presence of typical or atypical symptoms. In GERD patients, the presence of mucosal alterations in endoscopy is detected in up to 30% of individuals. The clinical presentation of GERD patients may be complex and their management is challenging, due to the heterogeneous clinical presentation. The present review has been performed searching all relevant articles in this field, over the past years, using PubMed database. AREAS COVERED The diagnosis and management of GERD have been significantly improved in the last years due to the increasing availability of reflux monitoring techniques and the implementation of new procedures in the therapeutic armamentarium. Beside traditional impedance-pH variables, new metrics have been developed, increasing the diagnostic yield of reflux monitoring and better predicting the treatment response. Traditional pharmacological treatments include acid-suppressive-therapy and/or anti-acid. On the other hand, surgical treatment and, more recently, endoscopic procedures represent a promising field in the therapeutic approach. EXPERT OPINION Diagnosis and treatment of GERD still represent a challenging area. However, we believe that an accurate upfront evaluation is, nowadays, necessary in addressing patients with GERD to a more accurate diagnosis as well as to the best treatment options.
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Affiliation(s)
- Mentore Ribolsi
- Unit of Gastroenterology and Digestive Endoscopy, Campus Bio Medico University, Rome, Italy
| | - Edoardo Savarino
- Gastroenterology Unit, Department of Surgery, Oncology and Gastroenterology, University of Padua, Padova, Italy
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17
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Sinonquel P, Vermeire S, Maes F, Bisschops R. Advanced Imaging in Gastrointestinal Endoscopy: A Literature Review of the Current State of the Art. GE PORTUGUESE JOURNAL OF GASTROENTEROLOGY 2023; 30:175-191. [PMID: 37387720 PMCID: PMC10305270 DOI: 10.1159/000527083] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 09/11/2022] [Indexed: 01/03/2025]
Abstract
BACKGROUND AND AIMS Gastrointestinal (GI) endoscopy has known a great evolution in the last decades. Imaging techniques evolved from imaging with only standard white light endoscopes toward high-definition resolution endoscopes and the use of multiple color enhancement techniques, over to automated endoscopic assessment systems based on artificial intelligence. This narrative literature review aimed to provide a detailed overview on the latest evolutions within the field of advanced GI endoscopy, mainly focusing on the screening, diagnosis, and surveillance of common upper and lower GI pathology. METHODS This review comprises only literature about screening, diagnosis, and surveillance strategies using advanced endoscopic imaging techniques published in (inter)national peer-reviewed journals and written in English. Studies with only adult patients included were selected. A search was performed using MESH terms: dye-based chromoendoscopy, virtual chromoendoscopy, video enhancement technique, upper GI tract, lower GI tract, Barrett's esophagus, esophageal squamous cell carcinoma, gastric cancer, colorectal polyps, inflammatory bowel disease, artificial intelligence. This review does not elaborate on the therapeutic application or impact of advanced GI endoscopy. CONCLUSIONS Focusing on current and future applications and evolutions in the field of both upper and lower GI advanced endoscopy, this overview is a practical but detailed projection of the latest developments. Within this review, an active leap toward artificial intelligence and its recent developments in GI endoscopy was made. Additionally, the literature is weighted against the current international guidelines and assessed for its potential positive future impact.
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Affiliation(s)
- Pieter Sinonquel
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
- Department of Translational Research in Gastrointestinal Diseases (TARGID), KU Leuven, Leuven, Belgium
| | - Séverine Vermeire
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
- Department of Translational Research in Gastrointestinal Diseases (TARGID), KU Leuven, Leuven, Belgium
| | - Frederik Maes
- Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
- Department of Translational Research in Gastrointestinal Diseases (TARGID), KU Leuven, Leuven, Belgium
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18
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Hussein M, Lines D, González-Bueno Puyal J, Kader R, Bowman N, Sehgal V, Toth D, Ahmad OF, Everson M, Esteban JM, Bisschops R, Banks M, Haefner M, Mountney P, Stoyanov D, Lovat LB, Haidry R. Computer-aided characterization of early cancer in Barrett's esophagus on i-scan magnification imaging: a multicenter international study. Gastrointest Endosc 2023; 97:646-654. [PMID: 36460087 PMCID: PMC10590905 DOI: 10.1016/j.gie.2022.11.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/08/2022] [Accepted: 11/18/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND AND AIMS We aimed to develop a computer-aided characterization system that could support the diagnosis of dysplasia in Barrett's esophagus (BE) on magnification endoscopy. METHODS Videos were collected in high-definition magnification white-light and virtual chromoendoscopy with i-scan (Pentax Hoya, Japan) imaging in patients with dysplastic and nondysplastic BE (NDBE) from 4 centers. We trained a neural network with a Resnet101 architecture to classify frames as dysplastic or nondysplastic. The network was tested on 3 different scenarios: high-quality still images, all available video frames, and a selected sequence within each video. RESULTS Fifty-seven patients, each with videos of magnification areas of BE (34 dysplasia, 23 NDBE), were included. Performance was evaluated by a leave-1-patient-out cross-validation method. In all, 60,174 (39,347 dysplasia, 20,827 NDBE) magnification video frames were used to train the network. The testing set included 49,726 i-scan-3/optical enhancement magnification frames. On 350 high-quality still images, the network achieved a sensitivity of 94%, specificity of 86%, and area under the receiver operator curve (AUROC) of 96%. On all 49,726 available video frames, the network achieved a sensitivity of 92%, specificity of 82%, and AUROC of 95%. On a selected sequence of frames per case (total of 11,471 frames), we used an exponentially weighted moving average of classifications on consecutive frames to characterize dysplasia. The network achieved a sensitivity of 92%, specificity of 84%, and AUROC of 96%. The mean assessment speed per frame was 0.0135 seconds (SD ± 0.006). CONCLUSION Our network can characterize BE dysplasia with high accuracy and speed on high-quality magnification images and sequence of video frames, moving it toward real-time automated diagnosis.
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Affiliation(s)
- Mohamed Hussein
- Division of Surgery and Interventional Sciences, University College London, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.
| | | | - Juana González-Bueno Puyal
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Odin Vision, UK
| | - Rawen Kader
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK
| | - Nicola Bowman
- Division of Surgery and Interventional Sciences, University College London, UK
| | - Vinay Sehgal
- Department of Gastroenterology, University College London Hospital, UK
| | | | - Omer F Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Department of Gastroenterology, University College London Hospital, UK
| | - Martin Everson
- Division of Surgery and Interventional Sciences, University College London, UK
| | - Jose Miguel Esteban
- Department of Gastroenterology and Hepatology, Clínico San Carlos, Madrid, Spain
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
| | - Matthew Banks
- Department of Gastroenterology, University College London Hospital, UK
| | - Michael Haefner
- Krankenhaus der Barmherzigen Schwestern, Department of Internal Medicine II, Vienna, Austria
| | | | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK
| | - Laurence B Lovat
- Division of Surgery and Interventional Sciences, University College London, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Department of Gastroenterology, University College London Hospital, UK
| | - Rehan Haidry
- Division of Surgery and Interventional Sciences, University College London, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Department of Gastroenterology, University College London Hospital, UK
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19
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Abdelrahim M, Saiko M, Maeda N, Hossain E, Alkandari A, Subramaniam S, Parra-Blanco A, Sanchez-Yague A, Coron E, Repici A, Bhandari P. Development and validation of artificial neural networks model for detection of Barrett's neoplasia: a multicenter pragmatic nonrandomized trial (with video). Gastrointest Endosc 2023; 97:422-434. [PMID: 36283443 DOI: 10.1016/j.gie.2022.10.031] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/30/2022] [Accepted: 10/16/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND AND AIMS The aim of this study was to develop and externally validate a computer-aided detection (CAD) system for the detection and localization of Barrett's neoplasia and assess its performance compared with that of general endoscopists in a statistically powered multicenter study by using real-time video sequences. METHODS In phase 1, the hybrid visual geometry group 16-SegNet model was trained by the use of 75,198 images and videos (96 patients) of neoplastic and 1,014,973 images and videos (65 patients) of nonneoplastic Barrett's esophagus. In phase 2, image-based validation was performed on a separate dataset of 107 images (20 patients) of neoplastic and 364 images (14 patients) of nonneoplastic Barrett's esophagus. In phase 3 (video-based external validation) we designed a real-time video-based study with 32 videos (32 patients) of neoplastic and 43 videos (43 patients) of nonneoplastic Barrett's esophagus from 4 European centers to compare the performance of the CAD model with that of 6 nonexpert endoscopists. The primary endpoint was the sensitivity of CAD diagnosis of Barrett's neoplasia. RESULTS In phase 2, CAD detected Barrett's neoplasia with sensitivity, specificity, and accuracy of 95.3%, 94.5%, and 94.7%, respectively. In phase 3, the CAD system detected Barrett's neoplasia with sensitivity, specificity, negative predictive value, and accuracy of 93.8%, 90.7%, 95.1%, and 92.0%, respectively, compared with the endoscopists' performance of 63.5%, 77.9%, 74.2%, and 71.8%, respectively (P < .05 in all parameters). The CAD system localized neoplastic lesions with accuracy, mean precision, and mean intersection over union of 100%, 0.62, and 0.54, respectively, when compared with at least 1 of the expert markings. The processing speed of the CAD detection and localization were 5 ms/image and 33 ms/image, respectively. CONCLUSION To our knowledge, this is the first study describing external (multicenter) validation of AI algorithms for the detection of Barrett's neoplasia on real-time endoscopic videos. The CAD system in this study significantly outperformed nonexpert endoscopists on real-time video-based assessment, achieving >90% sensitivity for neoplasia detection. This result needs to be validated during real-time endoscopic assessment.
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Affiliation(s)
| | - Masahiro Saiko
- Biometrics Research Laboratories, NEC Corporation, Kawasaki, Japan
| | - Naoto Maeda
- Medical AI Research Department, NEC Corporation, Tokyo, Japan
| | - Ejaz Hossain
- Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | | | | | - Adolfo Parra-Blanco
- Nottingham Digestive Diseases Biomedical Research Centre, and Nottingham University Hospitals NHS Trust, Nottingham, UK
| | | | - Emmanuel Coron
- Centre Hospitalier Universitaire and Faculté de Médecine de Nantes, Nantes, France
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20
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Goenka MK, Afzalpurkar S, Jejurikar S, Rodge GA, Tiwari A. Role of artificial intelligence-guided esophagogastroduodenoscopy in assessing the procedural completeness and quality. Indian J Gastroenterol 2023; 42:128-135. [PMID: 36715841 DOI: 10.1007/s12664-022-01294-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 08/12/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND AND AIMS The quality of esophagogastroduodenoscopy (EGD) can have great impact on the detection of esophageal and gastric lesions, including malignancies. The aim of the study is to investigate the use of artificial intelligence (AI) during EGD by the endoscopists-in-training so that a real-time feedback can be provided, ensuring compliance to a pre-decided protocol for examination. METHODS This is an observational pilot study. The videos of the EGD procedure performed between August 1, 2021, and September 30, 2021, were prospectively analyzed using AI system. The assessment of completeness of the procedure was done based on the visualizsation of pre-defined 29 locations. Endoscopists were divided into two categories - whether they are in the training period (category A) or have competed their endoscopy training (category B). RESULTS A total of 277 procedures, which included 114 category-A and 163 category-B endoscopists, respectively, were included. Most commonly covered areas by the endoscopists were greater curvature of antrum (97.47%), second part of duodenum (96.75%), other parts of antrum such as the anterior, lesser curvature and the posterior aspect (96.75%, 94.95%, and 94.22%, respectively). Commonly missed or inadequately seen areas were vocal cords (99.28%), epiglottis (93.14%) and posterior, anterior, and lateral aspect of incisura (78.70%, 73.65%, and 73.53%, respectively). The good quality procedures were done predominantly by categoryB endoscopists (88.68% vs. 11.32%, p < 0.00001). CONCLUSION AI can play an important role in assessing the quality and completeness of EGD and can be a part of training of endoscopy in future.
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Affiliation(s)
- Mahesh Kumar Goenka
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India.
| | - Shivaraj Afzalpurkar
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
| | | | - Gajanan Ashokrao Rodge
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
| | - Awanish Tiwari
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
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21
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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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22
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Knabe M, Welsch L, Blasberg T, Müller E, Heilani M, Bergen C, Herrmann E, May A. Artificial intelligence-assisted staging in Barrett's carcinoma. Endoscopy 2022; 54:1191-1197. [PMID: 35354219 DOI: 10.1055/a-1811-9407] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND Artificial intelligence (AI) is increasingly being used to detect neoplasia and interpret endoscopic images. The T stage of Barrett's carcinoma is a major criterion for subsequent treatment decisions. Although endoscopic ultrasound is still the standard for preoperative staging, its value is debatable. Novel tools are required to assist with staging, to optimize results. This study aimed to investigate the accuracy of T stage of Barrett's carcinoma by an AI system based on endoscopic images. METHODS 1020 images (minimum one per patient, maximum three) from 577 patients with Barrett's adenocarcinoma were used for training and internal validation of a convolutional neural network. In all, 821 images were selected to train the model and 199 images were used for validation. RESULTS AI recognized Barrett's mucosa without neoplasia with an accuracy of 85 % (95 %CI 82.7-87.1). Mucosal cancer was identified with a sensitivity of 72 % (95 %CI 67.5-76.4), specificity of 64 % (95 %CI 60.0-68.4), and accuracy of 68 % (95 %CI 64.6-70.7). The sensitivity, specificity, and accuracy for early Barrett's neoplasia < T1b sm2 were 57 % (95 %CI 51.8-61.0), 77 % (95 %CI 72.3-80.2), and 67 % (95 %CI 63.4-69.5), respectively. More advanced stages (T3/T4) were diagnosed correctly with a sensitivity of 71 % (95 %CI 65.1-76.7) and specificity of 73 % (95 %CI 69.7-76.5). The overall accuracy was 73 % (95 %CI 69.6-75.5). CONCLUSIONS The AI system identified esophageal cancer with high accuracy, suggesting its potential to assist endoscopists in clinical decision making.
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Affiliation(s)
- Mate Knabe
- Department of Gastroenterology, Frankfurt University Hospital, Frankfurt, Germany
| | - Lukas Welsch
- Department of Gastroenterology, Frankfurt University Hospital, Frankfurt, Germany
| | - Tobias Blasberg
- Department of Gastroenterology, Sana Klinikum GmbH Offenbach, Offenbach, Germany
| | - Elisa Müller
- Department of Gastroenterology, Frankfurt University Hospital, Frankfurt, Germany
| | - Myriam Heilani
- Department of Gastroenterology, Frankfurt University Hospital, Frankfurt, Germany
| | - Christoph Bergen
- HMS Analytical Software GmbH, HMS Analytical Software, Heidelberg, Germany
| | - Eva Herrmann
- Department of Medicine, Institute of Biostatistics and Mathematical Modeling, Goethe University of Frankfurt, Frankfurt, Germany
| | - Andrea May
- Department of Medicine I, Asklepios Paulinen Klinik Wiesbaden, Wiesbaden, Germany
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23
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Hussein M, González‐Bueno Puyal J, Lines D, Sehgal V, Toth D, Ahmad OF, Kader R, Everson M, Lipman G, Fernandez‐Sordo JO, Ragunath K, Esteban JM, Bisschops R, Banks M, Haefner M, Mountney P, Stoyanov D, Lovat LB, Haidry R. A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks. United European Gastroenterol J 2022; 10:528-537. [PMID: 35521666 PMCID: PMC9278593 DOI: 10.1002/ueg2.12233] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 03/31/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND AND AIMS Seattle protocol biopsies for Barrett's Esophagus (BE) surveillance are labour intensive with low compliance. Dysplasia detection rates vary, leading to missed lesions. This can potentially be offset with computer aided detection. We have developed convolutional neural networks (CNNs) to identify areas of dysplasia and where to target biopsy. METHODS 119 Videos were collected in high-definition white light and optical chromoendoscopy with i-scan (Pentax Hoya, Japan) imaging in patients with dysplastic and non-dysplastic BE (NDBE). We trained an indirectly supervised CNN to classify images as dysplastic/non-dysplastic using whole video annotations to minimise selection bias and maximise accuracy. The CNN was trained using 148,936 video frames (31 dysplastic patients, 31 NDBE, two normal esophagus), validated on 25,161 images from 11 patient videos and tested on 264 iscan-1 images from 28 dysplastic and 16 NDBE patients which included expert delineations. To localise targeted biopsies/delineations, a second directly supervised CNN was generated based on expert delineations of 94 dysplastic images from 30 patients. This was tested on 86 i-scan one images from 28 dysplastic patients. FINDINGS The indirectly supervised CNN achieved a per image sensitivity in the test set of 91%, specificity 79%, area under receiver operator curve of 93% to detect dysplasia. Per-lesion sensitivity was 100%. Mean assessment speed was 48 frames per second (fps). 97% of targeted biopsy predictions matched expert and histological assessment at 56 fps. The artificial intelligence system performed better than six endoscopists. INTERPRETATION Our CNNs classify and localise dysplastic Barrett's Esophagus potentially supporting endoscopists during surveillance.
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Affiliation(s)
- Mohamed Hussein
- Division of Surgery and Interventional SciencesUniversity College LondonLondonUK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS)University College LondonLondonUK
- Department of GastroenterologyUniversity College London HospitalLondonUK
| | - Juana González‐Bueno Puyal
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS)University College LondonLondonUK
- Odin VisionLondonUK
| | | | - Vinay Sehgal
- Department of GastroenterologyUniversity College London HospitalLondonUK
| | | | - Omer F. Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS)University College LondonLondonUK
- Department of GastroenterologyUniversity College London HospitalLondonUK
| | - Rawen Kader
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS)University College LondonLondonUK
| | - Martin Everson
- Division of Surgery and Interventional SciencesUniversity College LondonLondonUK
| | - Gideon Lipman
- Division of Surgery and Interventional SciencesUniversity College LondonLondonUK
| | | | - Krish Ragunath
- NIHR Nottingham Digestive Diseases Biomedical Research CentreNottinghamUK
| | | | | | - Matthew Banks
- Department of GastroenterologyUniversity College London HospitalLondonUK
| | | | | | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS)University College LondonLondonUK
| | - Laurence B. Lovat
- Division of Surgery and Interventional SciencesUniversity College LondonLondonUK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS)University College LondonLondonUK
- Department of GastroenterologyUniversity College London HospitalLondonUK
| | - Rehan Haidry
- Division of Surgery and Interventional SciencesUniversity College LondonLondonUK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS)University College LondonLondonUK
- Department of GastroenterologyUniversity College London HospitalLondonUK
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24
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Yoo BS, Houston KV, D'Souza SM, Elmahdi A, Davis I, Vilela A, Parekh PJ, Johnson DA. Advances and horizons for artificial intelligence of endoscopic screening and surveillance of gastric and esophageal disease. Artif Intell Med Imaging 2022; 3:70-86. [DOI: 10.35711/aimi.v3.i3.70] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/18/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
Affiliation(s)
- Byung Soo Yoo
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Kevin V Houston
- Department of Internal Medicine, Virginia Commonwealth University, Richmond, VA 23298, United States
| | - Steve M D'Souza
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Alsiddig Elmahdi
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Isaac Davis
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Ana Vilela
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Parth J Parekh
- Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - David A Johnson
- Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
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25
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Luo D, Kuang F, Du J, Zhou M, Liu X, Luo X, Tang Y, Li B, Su S. Artificial Intelligence-Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis. Front Oncol 2022; 12:855175. [PMID: 35756602 PMCID: PMC9229174 DOI: 10.3389/fonc.2022.855175] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022] Open
Abstract
Objective The aim of this study was to assess the diagnostic ability of artificial intelligence (AI) in the detection of early upper gastrointestinal cancer (EUGIC) using endoscopic images. Methods Databases were searched for studies on AI-assisted diagnosis of EUGIC using endoscopic images. The pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were calculated. Results Overall, 34 studies were included in our final analysis. Among the 17 image-based studies investigating early esophageal cancer (EEC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.95 (95% CI, 0.95–0.96), 0.95 (95% CI, 0.94–0.95), 10.76 (95% CI, 7.33–15.79), 0.07 (95% CI, 0.04–0.11), and 173.93 (95% CI, 81.79–369.83), respectively. Among the seven patient-based studies investigating EEC detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.94 (95% CI, 0.91–0.96), 0.90 (95% CI, 0.88–0.92), 6.14 (95% CI, 2.06–18.30), 0.07 (95% CI, 0.04–0.11), and 69.13 (95% CI, 14.73–324.45), respectively. Among the 15 image-based studies investigating early gastric cancer (EGC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.94, 0.87 (95% CI, 0.87–0.88), 0.88 (95% CI, 0.87–0.88), 7.20 (95% CI, 4.32–12.00), 0.14 (95% CI, 0.09–0.23), and 48.77 (95% CI, 24.98–95.19), respectively. Conclusions On the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).
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Affiliation(s)
- De Luo
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Fei Kuang
- Department of General Surgery, Changhai Hospital of The Second Military Medical University, Shanghai, China
| | - Juan Du
- Department of Clinical Medicine, Southwest Medical University, Luzhou, China
| | - Mengjia Zhou
- Department of Ultrasound, Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiangdong Liu
- Department of Hepatobiliary Surgery, Zigong Fourth People's Hospital, Zigong, China
| | - Xinchen Luo
- Department of Gastroenterology, Zigong Third People's Hospital, Zigong, China
| | - Yong Tang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Li
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Song Su
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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26
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Rao BH, Trieu JA, Nair P, Gressel G, Venu M, Venu RP. Artificial intelligence in endoscopy: More than what meets the eye in screening colonoscopy and endosonographic evaluation of pancreatic lesions. Artif Intell Gastrointest Endosc 2022; 3:16-30. [DOI: 10.37126/aige.v3.i3.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/07/2022] [Accepted: 05/07/2022] [Indexed: 02/06/2023] Open
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27
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Tan JL, Chinnaratha MA, Woodman R, Martin R, Chen HT, Carneiro G, Singh R. Diagnostic Accuracy of Artificial Intelligence (AI) to Detect Early Neoplasia in Barrett's Esophagus: A Non-comparative Systematic Review and Meta-Analysis. Front Med (Lausanne) 2022; 9:890720. [PMID: 35814747 PMCID: PMC9258946 DOI: 10.3389/fmed.2022.890720] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/27/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND AND AIMS Artificial Intelligence (AI) is rapidly evolving in gastrointestinal (GI) endoscopy. We undertook a systematic review and meta-analysis to assess the performance of AI at detecting early Barrett's neoplasia. METHODS We searched Medline, EMBASE and Cochrane Central Register of controlled trials database from inception to the 28th Jan 2022 to identify studies on the detection of early Barrett's neoplasia using AI. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies - 2 (QUADAS-2). A random-effects model was used to calculate pooled sensitivity, specificity, and diagnostics odds ratio (DOR). Forest plots and a summary of the receiving operating characteristics (SROC) curves displayed the outcomes. Heterogeneity was determined by I 2, Tau2 statistics and p-value. The funnel plots and Deek's test were used to assess publication bias. RESULTS Twelve studies comprising of 1,361 patients (utilizing 532,328 images on which the various AI models were trained) were used. The SROC was 0.94 (95% CI: 0.92-0.96). Pooled sensitivity, specificity and diagnostic odds ratio were 90.3% (95% CI: 87.1-92.7%), 84.4% (95% CI: 80.2-87.9%) and 48.1 (95% CI: 28.4-81.5), respectively. Subgroup analysis of AI models trained only on white light endoscopy was similar with pooled sensitivity and specificity of 91.2% (95% CI: 85.7-94.7%) and 85.1% (95% CI: 81.6%-88.1%), respectively. CONCLUSIONS AI is highly accurate at detecting early Barrett's neoplasia and validated for patients with at least high-grade dysplasia and above. Further well-designed prospective randomized controlled studies of all histopathological subtypes of early Barrett's neoplasia are needed to confirm these findings further.
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Affiliation(s)
- Jin Lin Tan
- Department of Gastroenterology and Hepatology, Lyell McEwin Hospital, SA Health, Elizabeth Vale, SA, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
| | - Mohamed Asif Chinnaratha
- Department of Gastroenterology and Hepatology, Lyell McEwin Hospital, SA Health, Elizabeth Vale, SA, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
| | - Richard Woodman
- Flinders Centre for Epidemiology and Biostatistics, College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Rory Martin
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia
| | - Hsiang-Ting Chen
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia
| | - Gustavo Carneiro
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia
| | - Rajvinder Singh
- Department of Gastroenterology and Hepatology, Lyell McEwin Hospital, SA Health, Elizabeth Vale, SA, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
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28
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Minchenberg SB, Walradt T, Glissen Brown JR. Scoping out the future: The application of artificial intelligence to gastrointestinal endoscopy. World J Gastrointest Oncol 2022; 14:989-1001. [PMID: 35646286 PMCID: PMC9124983 DOI: 10.4251/wjgo.v14.i5.989] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 06/21/2021] [Accepted: 04/21/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a quickly expanding field in gastrointestinal endoscopy. Although there are a myriad of applications of AI ranging from identification of bleeding to predicting outcomes in patients with inflammatory bowel disease, a great deal of research has focused on the identification and classification of gastrointestinal malignancies. Several of the initial randomized, prospective trials utilizing AI in clinical medicine have centered on polyp detection during screening colonoscopy. In addition to work focused on colorectal cancer, AI systems have also been applied to gastric, esophageal, pancreatic, and liver cancers. Despite promising results in initial studies, the generalizability of most of these AI systems have not yet been evaluated. In this article we review recent developments in the field of AI applied to gastrointestinal oncology.
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Affiliation(s)
- Scott B Minchenberg
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
| | - Trent Walradt
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
| | - Jeremy R Glissen Brown
- Division of Gastroenterology, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
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29
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Endoscopic Management of Barrett's Esophagus. Dig Dis Sci 2022; 67:1469-1479. [PMID: 35226224 DOI: 10.1007/s10620-022-07395-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/04/2022] [Indexed: 12/09/2022]
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Abstract
Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.
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Affiliation(s)
- Yutaka Okagawa
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Department of Gastroenterology, Tonan Hospital, Sapporo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ichiro Oda
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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31
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Sharma P, Hassan C. Artificial Intelligence and Deep Learning for Upper Gastrointestinal Neoplasia. Gastroenterology 2022; 162:1056-1066. [PMID: 34902362 DOI: 10.1053/j.gastro.2021.11.040] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 11/09/2021] [Accepted: 11/19/2021] [Indexed: 12/24/2022]
Abstract
Upper gastrointestinal (GI) neoplasia account for 35% of GI cancers and 1.5 million cancer-related deaths every year. Despite its efficacy in preventing cancer mortality, diagnostic upper GI endoscopy is affected by a substantial miss rate of neoplastic lesions due to failure to recognize a visible lesion or imperfect navigation. This may be offset by the real-time application of artificial intelligence (AI) for detection (computer-aided detection [CADe]) and characterization (computer-aided diagnosis [CADx]) of upper GI neoplasia. Stand-alone performance of CADe for esophageal squamous cell neoplasia, Barrett's esophagus-related neoplasia, and gastric cancer showed promising accuracy, sensitivity ranging between 83% and 93%. However, incorporation of CADe/CADx in clinical practice depends on several factors, such as possible bias in the training or validation phases of these algorithms, its interaction with human endoscopists, and clinical implications of false-positive results. The aim of this review is to guide the clinician across the multiple steps of AI development in clinical practice.
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Affiliation(s)
- Prateek Sharma
- University of Kansas School of Medicine, Kansas City, Missouri; Kansas City Veterans Affairs Medical Center, Kansas City, Missouri
| | - Cesare Hassan
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center-IRCCS, Endoscopy Unit, Rozzano, Italy.
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32
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Ishimura N, Okimoto E, Shibagaki K, Ishihara S. Endoscopic diagnosis and screening of Barrett's esophagus: Inconsistency of diagnostic criteria between Japan and Western countries. DEN OPEN 2022; 2:e73. [PMID: 35310704 PMCID: PMC8828243 DOI: 10.1002/deo2.73] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/10/2021] [Accepted: 10/16/2021] [Indexed: 11/11/2022]
Abstract
Barrett's esophagus (BE) is an endoscopically identifiable premalignant condition for esophageal adenocarcinoma (EAC). To diagnose BE precisely, careful inspection of the anatomic landmarks, including the esophagogastric junction and the squamocolumnar junction is important. The distal end of the palisade vessels and the proximal end of the gastric folds are used as the landmark of the esophagogastric junction in endoscopic diagnosis, with the latter solely used internationally, except in some Asian countries, including Japan. In addition, the diagnostic criteria adopted internationally for BE are inconsistent, particularly between Japan and Western countries. Recently updated guidelines in Western countries have included length criteria, with a 1‐cm threshold of columnar epithelium by endoscopic observation and/or histologic confirmation of the presence of specialized intestinal metaplasia. Since BE is endoscopically diagnosed at any length without histologic assessment in Japan, the reported prevalence of short‐segment BE is very high in Japan compared with that in Western countries. Although guidelines on screening exist for BE, the current strategies based on the presence of chronic gastroesophageal reflux disease with multiple risk factors may miss the opportunity for early detection of EAC. Indeed, up to 40% of patients with EAC have no history of chronic gastroesophageal reflux disease. To discuss BE on the same footing worldwide, standardization of diagnostic criteria, screening indication, and establishment of effective techniques for detecting dysplastic lesions are eagerly awaited. Japanese guidelines for BE should be revised regarding the length criteria, including the minimum length and long‐segment BE, in line with the recently updated Western guidelines.
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Affiliation(s)
- Norihisa Ishimura
- Second Department of Internal Medicine Shimane University Faculty of Medicine Shimane Japan
| | - Eiko Okimoto
- Second Department of Internal Medicine Shimane University Faculty of Medicine Shimane Japan
| | - Kotaro Shibagaki
- Division of Gastrointestinal Endoscopy Shimane University Hospital Shimane Japan
| | - Shunji Ishihara
- Second Department of Internal Medicine Shimane University Faculty of Medicine Shimane Japan
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33
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Buendgens L, Cifci D, Ghaffari Laleh N, van Treeck M, Koenen MT, Zimmermann HW, Herbold T, Lux TJ, Hann A, Trautwein C, Kather JN. Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy. Sci Rep 2022; 12:4829. [PMID: 35318364 PMCID: PMC8941159 DOI: 10.1038/s41598-022-08773-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 03/03/2022] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence (AI) is widely used to analyze gastrointestinal (GI) endoscopy image data. AI has led to several clinically approved algorithms for polyp detection, but application of AI beyond this specific task is limited by the high cost of manual annotations. Here, we show that a weakly supervised AI can be trained on data from a clinical routine database to learn visual patterns of GI diseases without any manual labeling or annotation. We trained a deep neural network on a dataset of N = 29,506 gastroscopy and N = 18,942 colonoscopy examinations from a large endoscopy unit serving patients in Germany, the Netherlands and Belgium, using only routine diagnosis data for the 42 most common diseases. Despite a high data heterogeneity, the AI system reached a high performance for diagnosis of multiple diseases, including inflammatory, degenerative, infectious and neoplastic diseases. Specifically, a cross-validated area under the receiver operating curve (AUROC) of above 0.70 was reached for 13 diseases, and an AUROC of above 0.80 was reached for two diseases in the primary data set. In an external validation set including six disease categories, the AI system was able to significantly predict the presence of diverticulosis, candidiasis, colon and rectal cancer with AUROCs above 0.76. Reverse engineering the predictions demonstrated that plausible patterns were learned on the level of images and within images and potential confounders were identified. In summary, our study demonstrates the potential of weakly supervised AI to generate high-performing classifiers and identify clinically relevant visual patterns based on non-annotated routine image data in GI endoscopy and potentially other clinical imaging modalities.
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Affiliation(s)
- Lukas Buendgens
- Department of Medicine III, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany
| | - Didem Cifci
- Department of Medicine III, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany
| | - Narmin Ghaffari Laleh
- Department of Medicine III, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany
| | - Marko van Treeck
- Department of Medicine III, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany
| | - Maria T Koenen
- Department of Medicine III, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany
- Department of Medicine, Rhein-Maas-Klinikum, Würselen, Germany
| | - Henning W Zimmermann
- Department of Medicine III, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany
| | - Till Herbold
- Department of Visceral Surgery and Transplantation, University Hospital RWTH Aachen, Aachen, Germany
| | - Thomas Joachim Lux
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Christian Trautwein
- Department of Medicine III, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany.
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
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Spadaccini M, Vespa E, Chandrasekar VT, Desai M, Patel HK, Maselli R, Fugazza A, Carrara S, Anderloni A, Franchellucci G, De Marco A, Hassan C, Bhandari P, Sharma P, Repici A. Advanced imaging and artificial intelligence for Barrett's esophagus: What we should and soon will do. World J Gastroenterol 2022; 28:1113-1122. [PMID: 35431503 PMCID: PMC8985480 DOI: 10.3748/wjg.v28.i11.1113] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/12/2021] [Accepted: 02/12/2022] [Indexed: 02/06/2023] Open
Abstract
Barrett's esophagus (BE) is a well-established risk factor for esophageal adenocarcinoma. It is recommended that patients have regular endoscopic surveillance, with the ultimate goal of detecting early-stage neoplastic lesions before they can progress to invasive carcinoma. Detection of both dysplasia or early adenocarcinoma permits curative endoscopic treatments, and with this aim, thorough endoscopic assessment is crucial and improves outcomes. The burden of missed neoplasia in BE is still far from being negligible, likely due to inappropriate endoscopic surveillance. Over the last two decades, advanced imaging techniques, moving from traditional dye-spray chromoendoscopy to more practical virtual chromoendoscopy technologies, have been introduced with the aim to enhance neoplasia detection in BE. As witnessed in other fields, artificial intelligence (AI) has revolutionized the field of diagnostic endoscopy and is set to cover a pivotal role in BE as well. The aim of this commentary is to comprehensively summarize present evidence, recent research advances, and future perspectives regarding advanced imaging technology and AI in BE; the combination of computer-aided diagnosis to a widespread adoption of advanced imaging technologies is eagerly awaited. It will also provide a useful step-by-step approach for performing high-quality endoscopy in BE, in order to increase the diagnostic yield of endoscopy in clinical practice.
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Affiliation(s)
- Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| | - Edoardo Vespa
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| | | | - Madhav Desai
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, MO 66045, United States
| | - Harsh K Patel
- Department of Internal Medicine, Ochsner Clinic Foundation, New Orleans, LA 70124, United States
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| | - Alessandro Fugazza
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
| | - Silvia Carrara
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
| | - Andrea Anderloni
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
| | - Gianluca Franchellucci
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| | - Alessandro De Marco
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| | - Cesare Hassan
- Endoscopy Unit, Nuovo Regina Margherita Hospital, Roma 00153, Italy
| | - Pradeep Bhandari
- Department of Gastroenterology, Portsmouth Hospitals University NHS Trust, Portsmouth PO6 3LY, United Kingdom
- School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth PO6 3LY, United Kingdom
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, MO 66045, United States
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
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35
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Schmitz R, Werner R, Repici A, Bisschops R, Meining A, Zornow M, Messmann H, Hassan C, Sharma P, Rösch T. Artificial intelligence in GI endoscopy: stumbling blocks, gold standards and the role of endoscopy societies. Gut 2022; 71:451-454. [PMID: 33479051 DOI: 10.1136/gutjnl-2020-323115] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 01/04/2021] [Accepted: 01/05/2021] [Indexed: 02/06/2023]
Affiliation(s)
- Rüdiger Schmitz
- Interdisciplinary Endoscopy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Rene Werner
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alessandro Repici
- Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy.,Humanitas University, Department of Biomedical Sciences, Milan, Italy
| | - Raf Bisschops
- Gastroenterology, University Hospital Gasthuisberg, Leuven, Belgium
| | - Alexander Meining
- Department of Gastroenterology, University of Würzburg, Würzburg, Germany
| | - Michael Zornow
- Chair for Public and European Law, University of Göttingen, Göttingen, Germany
| | - Helmut Messmann
- Department of Gastroenterology, Universitätsklinikum Augsburg, Augsburg, Germany
| | - Cesare Hassan
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
| | - Prateek Sharma
- Division of Gastroenterology and Hepatology, Veterans Affairs Medical Center and University of Kansas, Lawrence, Kansas, USA
| | - Thomas Rösch
- Interdisciplinary Endoscopy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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36
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Visaggi P, Barberio B, Gregori D, Azzolina D, Martinato M, Hassan C, Sharma P, Savarino E, de Bortoli N. Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases. Aliment Pharmacol Ther 2022; 55:528-540. [PMID: 35098562 PMCID: PMC9305819 DOI: 10.1111/apt.16778] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/09/2022] [Accepted: 01/09/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Artificial intelligence (AI) has recently been applied to endoscopy and questionnaires for the evaluation of oesophageal diseases (ODs). AIM We performed a systematic review with meta-analysis to evaluate the performance of AI in the diagnosis of malignant and benign OD. METHODS We searched MEDLINE, EMBASE, EMBASE Classic and the Cochrane Library. A bivariate random-effect model was used to calculate pooled diagnostic efficacy of AI models and endoscopists. The reference tests were histology for neoplasms and the clinical and instrumental diagnosis for gastro-oesophageal reflux disease (GERD). The pooled area under the summary receiver operating characteristic (AUROC), sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR) and diagnostic odds ratio (DOR) were estimated. RESULTS For the diagnosis of Barrett's neoplasia, AI had AUROC of 0.90, sensitivity 0.89, specificity 0.86, PLR 6.50, NLR 0.13 and DOR 50.53. AI models' performance was comparable with that of endoscopists (P = 0.35). For the diagnosis of oesophageal squamous cell carcinoma, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.97, 0.95, 0.92, 12.65, 0.05 and DOR 258.36, respectively. In this task, AI performed better than endoscopists although without statistically significant differences. In the detection of abnormal intrapapillary capillary loops, the performance of AI was: AUROC 0.98, sensitivity 0.94, specificity 0.94, PLR 14.75, NLR 0.07 and DOR 225.83. For the diagnosis of GERD based on questionnaires, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.99, 0.97, 0.97, 38.26, 0.03 and 1159.6, respectively. CONCLUSIONS AI demonstrated high performance in the clinical and endoscopic diagnosis of OD.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology UnitDepartment of Translational Research and New Technologies in Medicine and SurgeryUniversity of PisaPisaItaly
| | - Brigida Barberio
- Division of GastroenterologyDepartment of Surgery, Oncology and GastroenterologyUniversity of PadovaPadovaItaly
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
| | - Danila Azzolina
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
- Department of Medical ScienceUniversity of FerraraFerraraItaly
| | - Matteo Martinato
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas UniversityVia Rita Levi Montalcini 420072 Pieve Emanuele, MilanItaly
- IRCCS Humanitas Research Hospitalvia Manzoni 5620089 Rozzano, MilanItaly
| | - Prateek Sharma
- University of Kansas School of Medicine and VA Medical CenterKansas CityMissouriUSA
| | - Edoardo Savarino
- Division of GastroenterologyDepartment of Surgery, Oncology and GastroenterologyUniversity of PadovaPadovaItaly
| | - Nicola de Bortoli
- Gastroenterology UnitDepartment of Translational Research and New Technologies in Medicine and SurgeryUniversity of PisaPisaItaly
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van Munster S, Nieuwenhuis E, Weusten BLAM, Alvarez Herrero L, Bogte A, Alkhalaf A, Schenk BE, Schoon EJ, Curvers W, Koch AD, van de Ven SEM, de Jonge PJF, Tang TJ, Nagengast WB, Peters FTM, Westerhof J, Houben MHMG, Bergman JJ, Pouw RE. Long-term outcomes after endoscopic treatment for Barrett's neoplasia with radiofrequency ablation ± endoscopic resection: results from the national Dutch database in a 10-year period. Gut 2022; 71:265-276. [PMID: 33753417 PMCID: PMC8762001 DOI: 10.1136/gutjnl-2020-322615] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 03/02/2021] [Accepted: 03/08/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVE Radiofrequency ablation (RFA)±endoscopic resection (ER) is the preferred treatment for early neoplasia in Barrett's oesophagus (BE). We aimed to report short-term and long-term outcomes for all 1384 patients treated in the Netherlands (NL) from 2008 to 2018, with uniform treatment and follow-up (FU) in a centralised setting. DESIGN Endoscopic therapy for early BE neoplasia in NL is centralised in nine expert centres with specifically trained endoscopists and pathologists that adhere to a joint protocol. Prospectively collected data are registered in a uniform database. Patients with low/high-grade dysplasia or low-risk cancer, were treated by ER of visible lesions followed by trimonthly RFA sessions of any residual BE until complete eradication of BE (CE-BE). Patients with ER alone were not included. RESULTS After ER (62% of cases; 43% low-risk cancers) and median 1 circumferential and 2 focal RFA (p25-p75 0-1; 1-2) per patient, CE-BE was achieved in 94% (1270/1348). Adverse events occurred in 21% (268/1386), most commonly oesophageal stenosis (15%), all were managed endoscopically. A total of 1154 patients with CE-BE were analysed for long-term outcomes. During median 43 months (22-69) and 4 endoscopies (1-5), 38 patients developed dysplastic recurrence (3%, annual recurrence risk 1%), all were detected as endoscopically visible abnormalities. Random biopsies from a normal appearing cardia showed intestinal metaplasia (IM) in 14% and neoplasia in 0%. A finding of IM in the cardia was reproduced during further FU in only 33%, none progressed to neoplasia. Frequent FU visits in the first year of FU were not associated with recurrence risk. CONCLUSION In a setting of centralised care, RFA±ER is effective for eradication of Barrett's related neoplasia and has remarkably low rates of dysplastic recurrence. Our data support more lenient FU intervals, with emphasis on careful endoscopic inspection. Random biopsies from neosquamous epithelium and cardia are of questionable value. NETHERLANDS TRIAL REGISTER NUMBER NL7039.
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Affiliation(s)
- Sanne van Munster
- Gastroenterology and Hepatology, Amsterdam UMC Locatie AMC, Amsterdam, North Holland, The Netherlands
- Gastroenterology and Hepatology, Sint Antonius Hospital, Nieuwegein, The Netherlands
| | - Esther Nieuwenhuis
- Gastroenterology and Hepatology, Amsterdam UMC Locatie AMC, Amsterdam, North Holland, The Netherlands
| | - Bas L A M Weusten
- Gastroenterology and Hepatology, Sint Antonius Hospital, Nieuwegein, The Netherlands
- Gastroenterology and Hepatology, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Auke Bogte
- Gastroenterology and Hepatology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alaa Alkhalaf
- Gastroenterology and Hepatology, Isala Klinieken, Zwolle, Overijssel, The Netherlands
| | - B E Schenk
- Gastroenterology and Hepatology, Isala Klinieken, Zwolle, Overijssel, The Netherlands
| | - Erik J Schoon
- Department of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Wouter Curvers
- Department of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Arjun D Koch
- Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | | | - Tjon J Tang
- Department of Gastroenterology and Hepatology, IJsselland Ziekenhuis, Capelle aan den IJssel, Zuid-Holland, The Netherlands
| | - Wouter B Nagengast
- Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Frans T M Peters
- Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jessie Westerhof
- Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Martin H M G Houben
- Gastroenterology and Hepatology, Haga Hospital, Den Haag, Zuid-Holland, The Netherlands
| | - Jacques Jghm Bergman
- Gastroenterology and Hepatology, Amsterdam UMC Locatie AMC, Amsterdam, North Holland, The Netherlands
| | - Roos E Pouw
- Gastroenterology and Hepatology, Amsterdam UMC Locatie AMC, Amsterdam, North Holland, The Netherlands
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Visaggi P, de Bortoli N, Barberio B, Savarino V, Oleas R, Rosi EM, Marchi S, Ribolsi M, Savarino E. Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases. J Clin Gastroenterol 2022; 56:23-35. [PMID: 34739406 PMCID: PMC9988236 DOI: 10.1097/mcg.0000000000001629] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Artificial intelligence (AI) has enormous potential to support clinical routine workflows and therefore is gaining increasing popularity among medical professionals. In the field of gastroenterology, investigations on AI and computer-aided diagnosis (CAD) systems have mainly focused on the lower gastrointestinal (GI) tract. However, numerous CAD tools have been tested also in upper GI disorders showing encouraging results. The main application of AI in the upper GI tract is endoscopy; however, the need to analyze increasing loads of numerical and categorical data in short times has pushed researchers to investigate applications of AI systems in other upper GI settings, including gastroesophageal reflux disease, eosinophilic esophagitis, and motility disorders. AI and CAD systems will be increasingly incorporated into daily clinical practice in the coming years, thus at least basic notions will be soon required among physicians. For noninsiders, the working principles and potential of AI may be as fascinating as obscure. Accordingly, we reviewed systematic reviews, meta-analyses, randomized controlled trials, and original research articles regarding the performance of AI in the diagnosis of both malignant and benign esophageal and gastric diseases, also discussing essential characteristics of AI.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Nicola de Bortoli
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Brigida Barberio
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
| | - Vincenzo Savarino
- Gastroenterology Unit, Department of Internal Medicine, University of Genoa, Genoa
| | - Roberto Oleas
- Ecuadorean Institute of Digestive Diseases, Guayaquil, Ecuador
| | - Emma M. Rosi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Santino Marchi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Mentore Ribolsi
- Department of Digestive Diseases, Campus Bio Medico University of Rome, Roma, Italy
| | - Edoardo Savarino
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
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Li Q, Liu BR. Application of artificial intelligence-assisted endoscopic detection of early esophageal cancer. Shijie Huaren Xiaohua Zazhi 2021; 29:1389-1395. [DOI: 10.11569/wcjd.v29.i24.1389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
In recent years, artificial intelligence (AI) combined with endoscopy has made an appearance in the diagnosis of early esophageal cancer (EC) and achieved satisfactory results. Due to the rapid progression and poor prognosis of EC, the early detection and diagnosis of EC are of great value for patient prognosis improvement. AI has been applied in the screening of early EC and has shown advantages; notably, it is more accurate than less-experienced endoscopists. In China, the detection of early EC depends on endoscopist expertise and is inevitably subject to interobserver variability. The excellent imaging recognition ability of AI is very suitable for the diagnosis and recognition of EC, thereby reducing the missed diagnosis and helping physicians to perform endoscopy better. This paper reviews the application and relevant progress of AI in the field of endoscopic detection of early EC (including squamous cell carcinoma and adenocarcinoma), with a focus on diagnostic performance of AI to identify different types of endoscopic images, such as sensitivity and specificity.
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Affiliation(s)
- Qing Li
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan Province, China
| | - Bing-Rong Liu
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan Province, China
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40
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Pan W, Li X, Wang W, Zhou L, Wu J, Ren T, Liu C, Lv M, Su S, Tang Y. Identification of Barrett's esophagus in endoscopic images using deep learning. BMC Gastroenterol 2021; 21:479. [PMID: 34920705 PMCID: PMC8684213 DOI: 10.1186/s12876-021-02055-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 12/06/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Development of a deep learning method to identify Barrett's esophagus (BE) scopes in endoscopic images. METHODS 443 endoscopic images from 187 patients of BE were included in this study. The gastroesophageal junction (GEJ) and squamous-columnar junction (SCJ) of BE were manually annotated in endoscopic images by experts. Fully convolutional neural networks (FCN) were developed to automatically identify the BE scopes in endoscopic images. The networks were trained and evaluated in two separate image sets. The performance of segmentation was evaluated by intersection over union (IOU). RESULTS The deep learning method was proved to be satisfying in the automated identification of BE in endoscopic images. The values of the IOU were 0.56 (GEJ) and 0.82 (SCJ), respectively. CONCLUSIONS Deep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the BE scope in endoscopic images. This automated recognition method helps clinicians to locate and recognize the scopes of BE in endoscopic examinations.
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Affiliation(s)
- Wen Pan
- Department of Digestion, West China Hospital of Sichuan University, Chengdu, 610054, Sichuan, China
- Department of Digestion, The Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region, Ximianqiao Street No.20, Chengdu, 610054, Sichuan, China
| | - Xujia Li
- Department of General Surgery (Hepatobiliary Surgery), The Affiliated Hospital of Southwest Medical University, Taiping Street No.25, Luzhou, 646000, Sichuan, China
| | - Weijia Wang
- School of Information and Software Engineering, University of Electronic Science and Technology of China, 4 North Jianshe Road, Chengdu, 610054, Sichuan, China
| | - Linjing Zhou
- School of Information and Software Engineering, University of Electronic Science and Technology of China, 4 North Jianshe Road, Chengdu, 610054, Sichuan, China
| | - Jiali Wu
- Department of Anesthesiology, The Affiliated Hospital of Southwest Medical University, Taiping Street No.25, Luzhou, 646000, Sichuan, China
| | - Tao Ren
- Department of Digestion, The Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region, Ximianqiao Street No.20, Chengdu, 610054, Sichuan, China
| | - Chao Liu
- Department of Digestion, The Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region, Ximianqiao Street No.20, Chengdu, 610054, Sichuan, China.
| | - Muhan Lv
- Department of Digestion, The Affiliated Hospital of Southwest Medical University, Taiping Street No.25, Luzhou, 646000, Sichuan, China.
| | - Song Su
- Department of General Surgery (Hepatobiliary Surgery), The Affiliated Hospital of Southwest Medical University, Taiping Street No.25, Luzhou, 646000, Sichuan, China.
| | - Yong Tang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, 4 North Jianshe Road, Chengdu, 610054, Sichuan, China.
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41
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Bang CS. Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders. THE KOREAN JOURNAL OF HELICOBACTER AND UPPER GASTROINTESTINAL RESEARCH 2021. [DOI: 10.7704/kjhugr.2021.0030] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
In the past, conventional machine learning was applied to analyze tabulated medical data while deep learning was applied to study afflictions such as gastrointestinal disorders. Neural networks were used to detect, classify, and delineate various images of lesions because the local feature selection and optimization of the deep learning model enabled accurate image analysis. With the accumulation of medical records, the evolution of computational power and graphics processing units, and the widespread use of open-source libraries in large-scale machine learning processes, medical artificial intelligence (AI) is overcoming its limitations. While early studies prioritized the automatic diagnosis of cancer or pre-cancerous lesions, the current expanded scope of AI includes benign lesions, quality control, and machine learning analysis of big data. However, the limited commercialization of medical AI and the need to justify its application in each field of research are restricting factors. Modeling assumes that observations follow certain statistical rules, and external validation checks whether assumption is correct or generalizable. Therefore, unused data are essential in the training or internal testing process to validate the performance of the established AI models. This article summarizes the studies on the application of AI models in upper gastrointestinal disorders. The current limitations and the perspectives on future development have also been discussed.
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42
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Li N, Jin SZ. Artificial intelligence and early esophageal cancer. Artif Intell Gastrointest Endosc 2021; 2:198-210. [DOI: 10.37126/aige.v2.i5.198] [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: 07/28/2021] [Revised: 09/23/2021] [Accepted: 10/27/2021] [Indexed: 02/06/2023] Open
Abstract
The development of esophageal cancer (EC) from early to advanced stage results in a high mortality rate and poor prognosis. Advanced EC not only poses a serious threat to the life and health of patients but also places a heavy economic burden on their families and society. Endoscopy is of great value for the diagnosis of EC, especially in the screening of Barrett’s esophagus and early EC. However, at present, endoscopy has a low diagnostic rate for early tumors. In recent years, artificial intelligence (AI) has made remarkable progress in the diagnosis of digestive system tumors, providing a new model for clinicians to diagnose and treat these tumors. In this review, we aim to provide a comprehensive overview of how AI can help doctors diagnose early EC and precancerous lesions and make clinical decisions based on the predicted results. We analyze and summarize the recent research on AI and early EC. We find that based on deep learning (DL) and convolutional neural network methods, the current computer-aided diagnosis system has gradually developed from in vitro image analysis to real-time detection and diagnosis. Based on powerful computing and DL capabilities, the diagnostic accuracy of AI is close to or better than that of endoscopy specialists. We also analyze the shortcomings in the current AI research and corresponding improvement strategies. We believe that the application of AI-assisted endoscopy in the diagnosis of early EC and precancerous lesions will become possible after the further advancement of AI-related research.
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Affiliation(s)
- Ning Li
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang Province, China
| | - Shi-Zhu Jin
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang Province, China
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021; 11:1719. [PMID: 34574060 PMCID: PMC8468082 DOI: 10.3390/diagnostics11091719] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is rapidly becoming an essential tool in the medical field as well as in daily life. Recent developments in deep learning, a subfield of AI, have brought remarkable advances in image recognition, which facilitates improvement in the early detection of cancer by endoscopy, ultrasonography, and computed tomography. In addition, AI-assisted big data analysis represents a great step forward for precision medicine. This review provides an overview of AI technology, particularly for gastroenterology, hepatology, and pancreatology, to help clinicians utilize AI in the near future.
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Affiliation(s)
- Akihiko Oka
- Department of Internal Medicine II, Faculty of Medicine, Shimane University, Izumo 693-8501, Shimane, Japan; (N.I.); (S.I.)
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Correia FP, Lourenço LC. Artificial intelligence application in diagnostic gastrointestinal endoscopy - Deus ex machina? World J Gastroenterol 2021; 27:5351-5361. [PMID: 34539137 PMCID: PMC8409168 DOI: 10.3748/wjg.v27.i32.5351] [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: 01/28/2021] [Revised: 05/15/2021] [Accepted: 07/19/2021] [Indexed: 02/06/2023] Open
Abstract
The close relationship of medicine with technology and the particular interest in this symbiosis in recent years has led to the development of several computed artificial intelligence (AI) systems aimed at various areas of medicine. A number of studies have demonstrated that those systems allow accurate diagnoses with histological precision, thus facilitating decision-making by clinicians in real time. In the field of gastroenterology, AI has been applied in the diagnosis of pathologies of the entire digestive tract and their attached glands, and are increasingly accepted for the detection of colorectal polyps and confirming their histological classification. Studies have shown high accuracy, sensitivity, and specificity in relation to expert endoscopists, and mainly in relation to those with less experience. Other applications that are increasingly studied and with very promising results are the investigation of dysplasia in patients with Barrett's esophagus and the endoscopic and histological assessment of colon inflammation in patients with ulcerative colitis. In some cases AI is thus better than or at least equal to human abilities. However, additional studies are needed to reinforce the existing data, and mainly to determine the applicability of this technology in other indications. This review summarizes the state of the art of AI in gastroenterological pathology.
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Affiliation(s)
- Fábio Pereira Correia
- Department of Gastroenterology, Hospital Prof. Dr Fernando Fonseca, Lisbon 2720-276, Portugal
| | - Luís Carvalho Lourenço
- Department of Gastroenterology, Hospital Prof. Dr Fernando Fonseca, Lisbon 2720-276, Portugal
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Yang H, Hu B. Recent advances in early esophageal cancer: diagnosis and treatment based on endoscopy. Postgrad Med 2021; 133:665-673. [PMID: 34030580 DOI: 10.1080/00325481.2021.1934495] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 05/21/2021] [Indexed: 02/07/2023]
Abstract
Esophageal cancer (EC) often cannot be discovered in time because of its asymptomatic or symptom-atypical characteristics in early stage. The risk and probability of lymph node metastasis and distant metastasis increase correspondingly as the cancer aggressively invades deeper layers. Treatment regimens may be shifted to surgery and chemoradiotherapy (CRT) from endoscopic eradication therapy (EET) with poor quality of life and prognosis. It is imperative to identify dysplasia and EC early and enable early curative endoscopic treatments. Newer methods have been attempted in the clinical setting to achieve early detection at a more microscopic and precise level. Newer imaging techniques and artificial intelligence (AI) technology have been involved in targeted biopsies and will gradually unveil the visualization of pathology in the future. Early detection and diagnosis are the prerequisite to choose personal and precise treatment regimens. EET has also been undergoing development and improvement to benefit more patients as the first option or the firstly chosen alternative therapy, when compared with esophagectomy. More clinical studies are needed to provide more possibilities for EET.
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Affiliation(s)
- Hang Yang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Wu Hou District, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Wu Hou District, China
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Tokat M, van Tilburg L, Koch AD, Spaander MCW. Artificial Intelligence in Upper Gastrointestinal Endoscopy. Dig Dis 2021; 40:395-408. [PMID: 34348267 DOI: 10.1159/000518232] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 06/23/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND Over the past decade, several artificial intelligence (AI) systems are developed to assist in endoscopic assessment of (pre-)cancerous lesions of the gastrointestinal (GI) tract. In this review, we aimed to provide an overview of the possible indications of AI technology in upper GI endoscopy and hypothesize about potential challenges for its use in clinical practice. SUMMARY Application of AI in upper GI endoscopy has been investigated for several indications: (1) detection, characterization, and delineation of esophageal and gastric cancer (GC) and their premalignant conditions; (2) prediction of tumor invasion; and (3) detection of Helicobacter pylori. AI systems show promising results with an accuracy of up to 99% for the detection of superficial and advanced upper GI cancers. AI outperformed trainee and experienced endoscopists for the detection of esophageal lesions and atrophic gastritis. For GC, AI outperformed mid-level and trainee endoscopists but not expert endoscopists. KEY MESSAGES Application of artificial intelligence (AI) in upper gastrointestinal endoscopy may improve early diagnosis of esophageal and gastric cancer and may enable endoscopists to better identify patients eligible for endoscopic resection. The benefit of AI on the quality of upper endoscopy still needs to be demonstrated, while prospective trials are needed to confirm accuracy and feasibility during real-time daily endoscopy.
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Affiliation(s)
- Meltem Tokat
- Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Laurelle van Tilburg
- Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Arjun D Koch
- Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Manon C W Spaander
- Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
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Tziortziotis I, Laskaratos FM, Coda S. Role of Artificial Intelligence in Video Capsule Endoscopy. Diagnostics (Basel) 2021; 11:1192. [PMID: 34209029 PMCID: PMC8303156 DOI: 10.3390/diagnostics11071192] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
Capsule endoscopy (CE) has been increasingly utilised in recent years as a minimally invasive tool to investigate the whole gastrointestinal (GI) tract and a range of capsules are currently available for evaluation of upper GI, small bowel, and lower GI pathology. Although CE is undoubtedly an invaluable test for the investigation of small bowel pathology, it presents considerable challenges and limitations, such as long and laborious reading times, risk of missing lesions, lack of bowel cleansing score and lack of locomotion. Artificial intelligence (AI) seems to be a promising tool that may help improve the performance metrics of CE, and consequently translate to better patient care. In the last decade, significant progress has been made to apply AI in the field of endoscopy, including CE. Although it is certain that AI will find soon its place in day-to-day endoscopy clinical practice, there are still some open questions and barriers limiting its widespread application. In this review, we provide some general information about AI, and outline recent advances in AI and CE, issues around implementation of AI in medical practice and potential future applications of AI-aided CE.
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Affiliation(s)
- Ioannis Tziortziotis
- Endoscopy Unit, Digestive Diseases Centre, Queen’s Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK; (I.T.); (S.C.)
| | - Faidon-Marios Laskaratos
- Endoscopy Unit, Digestive Diseases Centre, Queen’s Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK; (I.T.); (S.C.)
| | - Sergio Coda
- Endoscopy Unit, Digestive Diseases Centre, Queen’s Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK; (I.T.); (S.C.)
- Photonics Group-Department of Physics, Imperial College London, Exhibition Rd, South Kensington, London SW7 2BX, UK
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Meining A. Endoneering: A new perspective for basic research in gastrointestinal endoscopy. United European Gastroenterol J 2021; 8:241-245. [PMID: 32310738 DOI: 10.1177/2050640620913433] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Affiliation(s)
- Alexander Meining
- Department of Gastroenterology, University Hospital of Würzburg, Würzburg, Germany.,Dedicated to Professor Meinhard Classen, who sadly passed away on 6 October 2019
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Bang CS. [Deep Learning in Upper Gastrointestinal Disorders: Status and Future Perspectives]. THE KOREAN JOURNAL OF GASTROENTEROLOGY 2021; 75:120-131. [PMID: 32209800 DOI: 10.4166/kjg.2020.75.3.120] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/01/2020] [Accepted: 03/02/2020] [Indexed: 12/18/2022]
Abstract
Artificial intelligence using deep learning has been applied to gastrointestinal disorders for the detection, classification, and delineation of various lesion images. With the accumulation of enormous medical records, the evolution of computation power with graphic processing units, and the widespread use of open-source libraries in large-scale machine learning processes, medical artificial intelligence is overcoming its traditional limitations. This paper explains the basic concepts of deep learning model establishment and summarizes previous studies on upper gastrointestinal disorders. The limitations and perspectives on future development are also discussed.
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Affiliation(s)
- Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea
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