|
1
|
Guo X, Pang L, Xu L, Zhu H, Du Y. Cascade-E-Yolov5s network for recognizing the ulcerative lesion subtypes in small intestinal. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2025; 96:035108. [PMID: 40099988 DOI: 10.1063/5.0235668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2024] [Accepted: 02/19/2025] [Indexed: 03/20/2025]
Abstract
In endoscopy, accurately diagnosing small intestinal ulcers presents significant challenges due to the complex morphology, varying number, and extensive distribution of the lesions, which contribute to a reduced accuracy in immediate diagnosis. The definitive diagnosis typically relies on pathological analysis, laboratory investigations, and prolonged follow-up, often leading to diagnostic delays. This study introduces the Cascade-E-Yolov5s network, designed to improve the efficiency and accuracy of immediate ulcer diagnosis by intelligently identifying ulcer subtypes. The Cascade-E-Yolov5s network integrates EfficientNet for the classification of ulcer lesion images and SimAM-Yolov5s for detecting lesions on these classified images. In the SimAM-Yolov5s component, EfficientNet replaces the traditional backbone structure of Yolov5s, and enhancements such as the SIoU loss function and a simple, parameter-free attention module are incorporated to optimize model performance. The study utilized a dataset comprising 4909 ulcer images from 684 patients at Shanghai Changhai Hospital, encompassing four ulcer types: cryptogenic multifocal ulcerous stenosing enteritis, non-specific ulcer, small intestinal tuberculosis, and Crohn's disease. The experimental findings indicate that Cascade-E-Yolov5s surpasses conventional detection networks, achieving an average detection precision of 86.46% and a mean average precision at the IoU of 0.5 (mAP@0.5) of 82.20%. This model notably enhances the detection efficiency of small intestinal ulcer subtypes, thereby assisting clinicians in making more precise immediate diagnoses.
Collapse
Affiliation(s)
- Xudong Guo
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Liying Pang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Lei Xu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Huiyun Zhu
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Yiqi Du
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| |
Collapse
|
|
2
|
Eidler P, Kopylov U, Ukashi O. Capsule Endoscopy in Inflammatory Bowel Disease: Evolving Role and Recent Advances. Gastrointest Endosc Clin N Am 2025; 35:73-102. [PMID: 39510694 DOI: 10.1016/j.giec.2024.07.002] [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: 11/15/2024]
Abstract
Capsule endoscopy has been proven as an efficient and accurate tool in the diagnosing and monitoring patients with inflammatory bowel disease, especially Crohn's disease (CD). The current European Crohn's and Colitis Organization guidelines recommend small bowel disease assessment in newly diagnosed CD, wherein small bowel capsule endoscopy (SBCE) is of prime importance. SBCE plays an essential role in assessing mucosal healing in patients with CD, serving as a monitoring tool in a treat to target strategy, and is capable of identifying high-risk patients for future flares.
Collapse
Affiliation(s)
- Pinhas Eidler
- Gastroenterology Institute, Sheba Medical Center Tel Hashomer, Ramat Gan 52621, Israel
| | - Uri Kopylov
- Gastroenterology Institute, Sheba Medical Center Tel Hashomer, Ramat Gan 52621, Israel; Faculty of Medical and Health Sciences, Tel-Aviv University, Ramat Aviv, Tel Aviv 69978, Israel
| | - Offir Ukashi
- Gastroenterology Institute, Sheba Medical Center Tel Hashomer, Ramat Gan 52621, Israel; Faculty of Medical and Health Sciences, Tel-Aviv University, Ramat Aviv, Tel Aviv 69978, Israel.
| |
Collapse
|
|
3
|
Spada C, Piccirelli S, Hassan C, Ferrari C, Toth E, González-Suárez B, Keuchel M, McAlindon M, Finta Á, Rosztóczy A, Dray X, Salvi D, Riccioni ME, Benamouzig R, Chattree A, Humphries A, Saurin JC, Despott EJ, Murino A, Johansson GW, Giordano A, Baltes P, Sidhu R, Szalai M, Helle K, Nemeth A, Nowak T, Lin R, Costamagna G. AI-assisted capsule endoscopy reading in suspected small bowel bleeding: a multicentre prospective study. Lancet Digit Health 2024; 6:e345-e353. [PMID: 38670743 DOI: 10.1016/s2589-7500(24)00048-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 02/20/2024] [Accepted: 03/04/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND Capsule endoscopy reading is time consuming, and readers are required to maintain attention so as not to miss significant findings. Deep convolutional neural networks can recognise relevant findings, possibly exceeding human performances and reducing the reading time of capsule endoscopy. Our primary aim was to assess the non-inferiority of artificial intelligence (AI)-assisted reading versus standard reading for potentially small bowel bleeding lesions (high P2, moderate P1; Saurin classification) at per-patient analysis. The mean reading time in both reading modalities was evaluated among the secondary endpoints. METHODS Patients aged 18 years or older with suspected small bowel bleeding (with anaemia with or without melena or haematochezia, and negative bidirectional endoscopy) were prospectively enrolled at 14 European centres. Patients underwent small bowel capsule endoscopy with the Navicam SB system (Ankon, China), which is provided with a deep neural network-based AI system (ProScan) for automatic detection of lesions. Initial reading was performed in standard reading mode. Second blinded reading was performed with AI assistance (the AI operated a first-automated reading, and only AI-selected images were assessed by human readers). The primary endpoint was to assess the non-inferiority of AI-assisted reading versus standard reading in the detection (diagnostic yield) of potentially small bowel bleeding P1 and P2 lesions in a per-patient analysis. This study is registered with ClinicalTrials.gov, NCT04821349. FINDINGS From Feb 17, 2021 to Dec 29, 2021, 137 patients were prospectively enrolled. 133 patients were included in the final analysis (73 [55%] female, mean age 66·5 years [SD 14·4]; 112 [84%] completed capsule endoscopy). At per-patient analysis, the diagnostic yield of P1 and P2 lesions in AI-assisted reading (98 [73·7%] of 133 lesions) was non-inferior (p<0·0001) and superior (p=0·0213) to standard reading (82 [62·4%] of 133; 95% CI 3·6-19·0). Mean small bowel reading time was 33·7 min (SD 22·9) in standard reading and 3·8 min (3·3) in AI-assisted reading (p<0·0001). INTERPRETATION AI-assisted reading might provide more accurate and faster detection of clinically relevant small bowel bleeding lesions than standard reading. FUNDING ANKON Technologies, China and AnX Robotica, USA provided the NaviCam SB system.
Collapse
Affiliation(s)
- Cristiano Spada
- Department of Medicine, Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy; Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Stefania Piccirelli
- Department of Medicine, Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy; Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | - Cesare Hassan
- IRCCS Humanitas Research Hospital, Department of Biomedical Sciences, Rozzano, Milan, Italy
| | - Clarissa Ferrari
- Unit of Research and Clinical Trials, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Ervin Toth
- Skåne University Hospital, Lund University, Department of Gastroenterology, Malmö, Sweden
| | - Begoña González-Suárez
- Hospital Clínic of Barcelona, Endoscopy Unit, Gastroenterology Department, Barcelona, Spain
| | - Martin Keuchel
- Agaplesion Bethesda Krankenhaus Bergedorf, Academic Teaching Hospital of the University of Hamburg, Clinic for Internal Medicine, Hamburg, Germany
| | - Marc McAlindon
- Sheffield Teaching Hospitals NHS Trust, Academic Department of Gastroenterology and Hepatology, Sheffield, UK; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Ádám Finta
- Endo-Kapszula Health Centre and Endoscopy Unit, Department of Gastroenterology, Székesfehérvár, Hungary
| | - András Rosztóczy
- University of Szeged, Department of Internal Medicine, Szeged, Hungary
| | - Xavier Dray
- Sorbonne University, Saint Antoine Hospital, APHP, Centre for Digestive Endoscopy, Paris, France
| | - Daniele Salvi
- Department of Medicine, Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy; Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Maria Elena Riccioni
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Digestive Endoscopy Unit, Rome, Italy
| | - Robert Benamouzig
- Hôpital Avicenne, Université Paris 13, Service de Gastroenterologie, Bobigny, France
| | - Amit Chattree
- South Tyneside and Sunderland NHS Foundation Trust, Gastroenterology, Stockton-on-Tees, UK
| | - Adam Humphries
- St Mark's Hospital and Academic Institute, Department of Gastroenterology, Middlesex, UK
| | - Jean-Christophe Saurin
- Hospices Civils de Lyon-Centre Hospitalier Universitaire, Gastroenterology Department, Lyon, France
| | - Edward J Despott
- The Royal Free Hospital and University College London (UCL) Institute for Liver and Digestive Health, Royal Free Unit for Endoscopy, London, UK
| | - Alberto Murino
- The Royal Free Hospital and University College London (UCL) Institute for Liver and Digestive Health, Royal Free Unit for Endoscopy, London, UK
| | | | - Antonio Giordano
- Hospital Clínic of Barcelona, Endoscopy Unit, Gastroenterology Department, Barcelona, Spain
| | - Peter Baltes
- Agaplesion Bethesda Krankenhaus Bergedorf, Academic Teaching Hospital of the University of Hamburg, Clinic for Internal Medicine, Hamburg, Germany
| | - Reena Sidhu
- Sheffield Teaching Hospitals NHS Trust, Academic Department of Gastroenterology and Hepatology, Sheffield, UK; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Milan Szalai
- Endo-Kapszula Health Centre and Endoscopy Unit, Department of Gastroenterology, Székesfehérvár, Hungary
| | - Krisztina Helle
- University of Szeged, Department of Internal Medicine, Szeged, Hungary
| | - Artur Nemeth
- Skåne University Hospital, Lund University, Department of Gastroenterology, Malmö, Sweden
| | | | - Rong Lin
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Department of Gastroenterology, Wuhan, China
| | - Guido Costamagna
- Department of Medicine, Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy; Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| |
Collapse
|
|
4
|
Li L, Yang L, Zhang B, Yan G, Bao Y, Zhu R, Li S, Wang H, Chen M, Jin C, Chen Y, Yu C. Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm. Clin Res Hepatol Gastroenterol 2024; 48:102334. [PMID: 38582328 DOI: 10.1016/j.clinre.2024.102334] [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: 03/20/2024] [Accepted: 04/04/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND In order to overcome the challenges of lesion detection in capsule endoscopy (CE), we improved the YOLOv5-based deep learning algorithm and established the CE-YOLOv5 algorithm to identify small bowel lesions captured by CE. METHODS A total of 124,678 typical abnormal images from 1,452 patients were enrolled to train the CE-YOLOv5 model. Then 298 patients with suspected small bowel lesions detected by CE were prospectively enrolled in the testing phase of the study. Small bowel images and videos from the above 298 patients were interpreted by the experts, non-experts and CE-YOLOv5, respectively. RESULTS The sensitivity of CE-YOLOv5 in diagnosing vascular lesions, ulcerated/erosive lesions, protruding lesions, parasite, diverticulum, active bleeding and villous lesions based on CE videos was 91.9 %, 92.2 %, 91.4 %, 93.1 %, 93.3 %, 95.1 %, and 100 % respectively. Furthermore, CE-YOLOv5 achieved specificity and accuracy of more than 90 % for all lesions. Compared with experts, the CE-YOLOv5 showed comparable overall sensitivity, specificity and accuracy (all P > 0.05). Compared with non-experts, the CE-YOLOv5 showed significantly higher overall sensitivity (P < 0.0001) and overall accuracy (P < 0.0001), and a moderately higher overall specificity (P = 0.0351). Furthermore, the time for AI-reading (5.62 ± 2.81 min) was significantly shorter than that for the other two groups (both P < 0.0001). CONCLUSIONS CE-YOLOv5 diagnosed small bowel lesions in CE videos with high sensitivity, specificity and accuracy, providing a reliable approach for automated lesion detection in real-world clinical practice.
Collapse
Affiliation(s)
- Lan Li
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China.
| | - Liping Yang
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Bingling Zhang
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Guofei Yan
- Zhejiang Center for Medical Device Evaluation, Hangzhou, China
| | - Yaqing Bao
- GBA Center for Medical Device Evaluation and Inspection, National Medical Products Administration, Shenzhen, China
| | - Renke Zhu
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Shengjie Li
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Huogen Wang
- Zhejiang Herymed Technology Co., Ltd, Hangzhou, China; Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Ming Chen
- Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Chaohui Jin
- Zhejiang Herymed Technology Co., Ltd, Hangzhou, China; Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Yishu Chen
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Chaohui Yu
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| |
Collapse
|
|
5
|
Choi KS, Park D, Kim JS, Cheung DY, Lee BI, Cho YS, Kim JI, Lee S, Lee HH. Deep learning in negative small-bowel capsule endoscopy improves small-bowel lesion detection and diagnostic yield. Dig Endosc 2024; 36:437-445. [PMID: 37612137 DOI: 10.1111/den.14670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 08/20/2023] [Indexed: 08/25/2023]
Abstract
OBJECTIVES Although several studies have shown the usefulness of artificial intelligence to identify abnormalities in small-bowel capsule endoscopy (SBCE) images, few studies have proven its actual clinical usefulness. Thus, the aim of this study was to examine whether meaningful findings could be obtained when negative SBCE videos were reanalyzed with a deep convolutional neural network (CNN) model. METHODS Clinical data of patients who received SBCE for suspected small-bowel bleeding at two academic hospitals between February 2018 and July 2020 were retrospectively collected. All SBCE videos read as negative were reanalyzed with the CNN algorithm developed in our previous study. Meaningful findings such as angioectasias and ulcers were finally decided after reviewing CNN-selected images by two gastroenterologists. RESULTS Among 202 SBCE videos, 103 (51.0%) were read as negative by humans. Meaningful findings were detected in 63 (61.2%) of these 103 videos after reanalyzing them with the CNN model. There were 79 red spots or angioectasias in 40 videos and 66 erosions or ulcers in 35 videos. After reanalysis, the diagnosis was changed for 10 (10.3%) patients who had initially negative SBCE results. During a mean follow-up of 16.5 months, rebleeding occurred in 19 (18.4%) patients. The rebleeding rate was 23.6% (13/55) for patients with meaningful findings and 16.1% (5/31) for patients without meaningful findings (P = 0.411). CONCLUSION Our CNN algorithm detected meaningful findings in negative SBCE videos that were missed by humans. The use of deep CNN for SBCE image reading is expected to compensate for human error.
Collapse
Affiliation(s)
- Kyung Seok Choi
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - DoGyeom Park
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Jin Su Kim
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Dae Young Cheung
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Bo-In Lee
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Young-Seok Cho
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jin Il Kim
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Seungchul Lee
- Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, Korea
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
- Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, Korea
| | - Han Hee Lee
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| |
Collapse
|
|
6
|
George AA, Tan JL, Kovoor JG, Lee A, Stretton B, Gupta AK, Bacchi S, George B, Singh R. Artificial intelligence in capsule endoscopy: development status and future expectations. MINI-INVASIVE SURGERY 2024. [DOI: 10.20517/2574-1225.2023.102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2025]
Abstract
In this review, we aim to illustrate the state-of-the-art artificial intelligence (AI) applications in the field of capsule endoscopy. AI has made significant strides in gastrointestinal imaging, particularly in capsule endoscopy - a non-invasive procedure for capturing gastrointestinal tract images. However, manual analysis of capsule endoscopy videos is labour-intensive and error-prone, prompting the development of automated computational algorithms and AI models. While currently serving as a supplementary observer, AI has the capacity to evolve into an autonomous, integrated reading system, potentially significantly reducing capsule reading time while surpassing human accuracy. We searched Embase, Pubmed, Medline, and Cochrane databases from inception to 06 Jul 2023 for studies investigating the use of AI for capsule endoscopy and screened retrieved records for eligibility. Quantitative and qualitative data were extracted and synthesised to identify current themes. In the search, 824 articles were collected, and 291 duplicates and 31 abstracts were deleted. After a double-screening process and full-text review, 106 publications were included in the review. Themes pertaining to AI for capsule endoscopy included active gastrointestinal bleeding, erosions and ulcers, vascular lesions and angiodysplasias, polyps and tumours, inflammatory bowel disease, coeliac disease, hookworms, bowel prep assessment, and multiple lesion detection. This review provides current insights into the impact of AI on capsule endoscopy as of 2023. AI holds the potential for faster and precise readings and the prospect of autonomous image analysis. However, careful consideration of diagnostic requirements and potential challenges is crucial. The untapped potential within vision transformer technology hints at further evolution and even greater patient benefit.
Collapse
|
|
7
|
Guo X, Xu L, Liu Z, Hao Y, Wang P, Zhu H, Du Y. Automated classification of ulcerative lesions in small intestine using densenet with channel attention and residual dilated blocks. Phys Med Biol 2024; 69:055017. [PMID: 38316034 DOI: 10.1088/1361-6560/ad2637] [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: 09/06/2023] [Accepted: 02/05/2024] [Indexed: 02/07/2024]
Abstract
Objective. Ulceration of the small intestine, which has a high incidence, includes Crohn's disease (CD), intestinal tuberculosis (ITB), primary small intestinal lymphoma (PSIL), cryptogenic multifocal ulcerous stenosing enteritis (CMUSE), and non-specific ulcer (NSU). However, the ulceration morphology can easily be misdiagnosed through enteroscopy.Approach. In this study, DRCA-DenseNet169, which is based on DenseNet169, with residual dilated blocks and a channel attention block, is proposed to identify CD, ITB, PSIL, CMUSE, and NSU intelligently. In addition, a novel loss function that incorporates dynamic weights is designed to enhance the precision of imbalanced datasets with limited samples. DRCA-Densenet169 was evaluated using 10883 enteroscopy images, including 5375 ulcer images and 5508 normal images, which were obtained from the Shanghai Changhai Hospital.Main results. DRCA-Densenet169 achieved an overall accuracy of 85.27% ± 0.32%, a weighted-precision of 83.99% ± 2.47%, a weighted-recall of 84.36% ± 0.88% and a weighted-F1-score of 84.07% ± 2.14%.Significance. The results demonstrate that DRCA-Densenet169 has high recognition accuracy and strong robustness in identifying different types of ulcers when obtaining immediate and preliminary diagnoses.
Collapse
Affiliation(s)
- Xudong Guo
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Lei Xu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Zhang Liu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Youguo Hao
- Department of Rehabilitation, Shanghai Putuo People's Hospital, Shanghai 200060, People's Republic of China
| | - Peng Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Huiyun Zhu
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai 200433, People's Republic of China
| | - Yiqi Du
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai 200433, People's Republic of China
| |
Collapse
|
|
8
|
Mota J, Almeida MJ, Mendes F, Martins M, Ribeiro T, Afonso J, Cardoso P, Cardoso H, Andrade P, Ferreira J, Mascarenhas M, Macedo G. From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy? Diagnostics (Basel) 2024; 14:291. [PMID: 38337807 PMCID: PMC10855436 DOI: 10.3390/diagnostics14030291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/09/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024] Open
Abstract
The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.
Collapse
Affiliation(s)
- Joana Mota
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Maria João Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Helder Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Patrícia Andrade
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal;
- Digestive Artificial Intelligence Development, R. Alfredo Allen 455-461, 4200-135 Porto, Portugal
| | - Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- ManopH Gastroenterology Clinic, R. de Sá da Bandeira 752, 4000-432 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| |
Collapse
|
|
9
|
Wang X, Hu X, Xu Y, Yong J, Li X, Zhang K, Gan T, Yang J, Rao N. A systematic review on diagnosis and treatment of gastrointestinal diseases by magnetically controlled capsule endoscopy and artificial intelligence. Therap Adv Gastroenterol 2023; 16:17562848231206991. [PMID: 37900007 PMCID: PMC10612444 DOI: 10.1177/17562848231206991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/21/2023] [Indexed: 10/31/2023] Open
Abstract
Background Magnetically controlled capsule endoscopy (MCCE) is a non-invasive, painless, comfortable, and safe equipment to diagnose gastrointestinal diseases (GID), partially overcoming the shortcomings of conventional endoscopy and wireless capsule endoscopy (WCE). With advancements in technology, the main technical parameters of MCCE have continuously been improved, and MCCE has become more intelligent. Objectives The aim of this systematic review was to summarize the research progress of MCCE and artificial intelligence (AI) in the diagnosis and treatment of GID. Data Sources and Methods We conducted a systematic search of PubMed and EMBASE for published studies on GID detection of MCCE, physical factors related to MCCE imaging quality, the application of AI in aiding MCCE, and its additional functions. We synergistically reviewed the included studies, extracted relevant data, and made comparisons. Results MCCE was confirmed to have the same performance as conventional gastroscopy and WCE in detecting common GID, while it lacks research in detecting early gastric cancer (EGC). The body position and cleanliness of the gastrointestinal tract are the main factors affecting imaging quality. The applications of AI in screening intestinal diseases have been comprehensive, while in the detection of common gastric diseases such as ulcers, it has been developed. MCCE can perform some additional functions, such as observations of drug behavior in the stomach and drug damage to the gastric mucosa. Furthermore, it can be improved to perform a biopsy. Conclusion This comprehensive review showed that the MCCE technology has made great progress, but studies on GID detection and treatment by MCCE are in the primary stage. Further studies are required to confirm the performance of MCCE.
Collapse
Affiliation(s)
- Xiaotong Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoming Hu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yongxue Xu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiahao Yong
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiang Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Kaixuan Zhang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Gan
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu, China
| | - Jinlin Yang
- Digestive Endoscopic Center of West China Hospital, Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, Chengdu, Sichuan Province 610017, China
| | - Nini Rao
- School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section Two, Jianshe North Road, Chengdu 610054, China
| |
Collapse
|
|
10
|
Musha A, Hasnat R, Mamun AA, Ping EP, Ghosh T. Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:7170. [PMID: 37631707 PMCID: PMC10459126 DOI: 10.3390/s23167170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023]
Abstract
Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.
Collapse
Affiliation(s)
- Ahmmad Musha
- Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh; (A.M.); (R.H.)
| | - Rehnuma Hasnat
- Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh; (A.M.); (R.H.)
| | - Abdullah Al Mamun
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia;
| | - Em Poh Ping
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia;
| | - Tonmoy Ghosh
- Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA;
| |
Collapse
|
|
11
|
Ukashi O, Soffer S, Klang E, Eliakim R, Ben-Horin S, Kopylov U. Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence. Gut Liver 2023; 17:516-528. [PMID: 37305947 PMCID: PMC10352070 DOI: 10.5009/gnl220507] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/23/2023] [Accepted: 01/30/2023] [Indexed: 06/13/2023] Open
Abstract
Video capsule endoscopy (VCE) of the small-bowel has been proven to accurately diagnose small-bowel inflammation and to predict future clinical flares among patients with Crohn's disease (CD). In 2017, the panenteric capsule (PillCam Crohn's system) was introduced for the first time, enabling a reliable evaluation of the whole small and large intestines. The great advantage of visualization of both parts of the gastrointestinal tract in a feasible and single procedure, holds a significant promise for patients with CD, enabling determination of the disease extent and severity, and potentially optimize disease management. In recent years, applications of machine learning, for VCE have been well studied, demonstrating impressive performance and high accuracy for the detection of various gastrointestinal pathologies, among them inflammatory bowel disease lesions. The use of artificial neural network models has been proven to accurately detect/classify and grade CD lesions, and shorten the VCE reading time, resulting in a less tedious process with a potential to minimize missed diagnosis and better predict clinical outcomes. Nevertheless, prospective, and real-world studies are essential to precisely examine artificial intelligence applications in real-life inflammatory bowel disease practice.
Collapse
Affiliation(s)
- Offir Ukashi
- Gastroenterology Institute, Sheba Medical Center, Tel Hashomer, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Internal Medicine A, Sheba Medical Center, Tel Hashomer, Israel
| | - Shelly Soffer
- Deep Vision Lab, Sheba Medical Center, Tel Hashomer, Israel
- Internal Medicine B, Assuta Medical Center, Ashdod, Israel
- Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Eyal Klang
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Deep Vision Lab, Sheba Medical Center, Tel Hashomer, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel
| | - Rami Eliakim
- Gastroenterology Institute, Sheba Medical Center, Tel Hashomer, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Shomron Ben-Horin
- Gastroenterology Institute, Sheba Medical Center, Tel Hashomer, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Uri Kopylov
- Gastroenterology Institute, Sheba Medical Center, Tel Hashomer, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| |
Collapse
|
|
12
|
Muchuchuti S, Viriri S. Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review. J Imaging 2023; 9:84. [PMID: 37103235 PMCID: PMC10145952 DOI: 10.3390/jimaging9040084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/02/2023] [Accepted: 04/07/2023] [Indexed: 04/28/2023] Open
Abstract
Millions of people are affected by retinal abnormalities worldwide. Early detection and treatment of these abnormalities could arrest further progression, saving multitudes from avoidable blindness. Manual disease detection is time-consuming, tedious and lacks repeatability. There have been efforts to automate ocular disease detection, riding on the successes of the application of Deep Convolutional Neural Networks (DCNNs) and vision transformers (ViTs) for Computer-Aided Diagnosis (CAD). These models have performed well, however, there remain challenges owing to the complex nature of retinal lesions. This work reviews the most common retinal pathologies, provides an overview of prevalent imaging modalities and presents a critical evaluation of current deep-learning research for the detection and grading of glaucoma, diabetic retinopathy, Age-Related Macular Degeneration and multiple retinal diseases. The work concluded that CAD, through deep learning, will increasingly be vital as an assistive technology. As future work, there is a need to explore the potential impact of using ensemble CNN architectures in multiclass, multilabel tasks. Efforts should also be expended on the improvement of model explainability to win the trust of clinicians and patients.
Collapse
Affiliation(s)
| | - Serestina Viriri
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 4001, South Africa
| |
Collapse
|
|
13
|
Dong Z, Wang J, Li Y, Deng Y, Zhou W, Zeng X, Gong D, Liu J, Pan J, Shang R, Xu Y, Xu M, Zhang L, Zhang M, Tao X, Zhu Y, Du H, Lu Z, Yao L, Wu L, Yu H. Explainable artificial intelligence incorporated with domain knowledge diagnosing early gastric neoplasms under white light endoscopy. NPJ Digit Med 2023; 6:64. [PMID: 37045949 PMCID: PMC10097818 DOI: 10.1038/s41746-023-00813-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 03/30/2023] [Indexed: 04/14/2023] Open
Abstract
White light endoscopy is the most pivotal tool for detecting early gastric neoplasms. Previous artificial intelligence (AI) systems were primarily unexplainable, affecting their clinical credibility and acceptability. We aimed to develop an explainable AI named ENDOANGEL-ED (explainable diagnosis) to solve this problem. A total of 4482 images and 296 videos with focal lesions from 3279 patients from eight hospitals were used for training, validating, and testing ENDOANGEL-ED. A traditional sole deep learning (DL) model was trained using the same dataset. The performance of ENDOANGEL-ED and sole DL was evaluated on six levels: internal and external images, internal and external videos, consecutive videos, and man-machine comparison with 77 endoscopists in videos. Furthermore, a multi-reader, multi-case study was conducted to evaluate the ENDOANGEL-ED's effectiveness. A scale was used to compare the overall acceptance of endoscopists to traditional and explainable AI systems. The ENDOANGEL-ED showed high performance in the image and video tests. In man-machine comparison, the accuracy of ENDOANGEL-ED was significantly higher than that of all endoscopists in internal (81.10% vs. 70.61%, p < 0.001) and external videos (88.24% vs. 78.49%, p < 0.001). With ENDOANGEL-ED's assistance, the accuracy of endoscopists significantly improved (70.61% vs. 79.63%, p < 0.001). Compared with the traditional AI, the explainable AI increased the endoscopists' trust and acceptance (4.42 vs. 3.74, p < 0.001; 4.52 vs. 4.00, p < 0.001). In conclusion, we developed a real-time explainable AI that showed high performance, higher clinical credibility, and acceptance than traditional DL models and greatly improved the diagnostic ability of endoscopists.
Collapse
Affiliation(s)
- Zehua Dong
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Junxiao Wang
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yanxia Li
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yunchao Deng
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Zhou
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaoquan Zeng
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Dexin Gong
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Liu
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jie Pan
- Department of Gastroenterology, Wenzhou Central Hospital, Wenzhou, China
| | - Renduo Shang
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Youming Xu
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ming Xu
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lihui Zhang
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mengjiao Zhang
- Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao Tao
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yijie Zhu
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hongliu Du
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zihua Lu
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liwen Yao
- Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lianlian Wu
- Renmin Hospital of Wuhan University, Wuhan, China.
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China.
| | - Honggang Yu
- Renmin Hospital of Wuhan University, Wuhan, China.
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China.
| |
Collapse
|
|
14
|
Chung J, Oh DJ, Park J, Kim SH, Lim YJ. Automatic Classification of GI Organs in Wireless Capsule Endoscopy Using a No-Code Platform-Based Deep Learning Model. Diagnostics (Basel) 2023; 13:diagnostics13081389. [PMID: 37189489 DOI: 10.3390/diagnostics13081389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/03/2023] [Accepted: 04/10/2023] [Indexed: 05/17/2023] Open
Abstract
The first step in reading a capsule endoscopy (CE) is determining the gastrointestinal (GI) organ. Because CE produces too many inappropriate and repetitive images, automatic organ classification cannot be directly applied to CE videos. In this study, we developed a deep learning algorithm to classify GI organs (the esophagus, stomach, small bowel, and colon) using a no-code platform, applied it to CE videos, and proposed a novel method to visualize the transitional area of each GI organ. We used training data (37,307 images from 24 CE videos) and test data (39,781 images from 30 CE videos) for model development. This model was validated using 100 CE videos that included "normal", "blood", "inflamed", "vascular", and "polypoid" lesions. Our model achieved an overall accuracy of 0.98, precision of 0.89, recall of 0.97, and F1 score of 0.92. When we validated this model relative to the 100 CE videos, it produced average accuracies for the esophagus, stomach, small bowel, and colon of 0.98, 0.96, 0.87, and 0.87, respectively. Increasing the AI score's cut-off improved most performance metrics in each organ (p < 0.05). To locate a transitional area, we visualized the predicted results over time, and setting the cut-off of the AI score to 99.9% resulted in a better intuitive presentation than the baseline. In conclusion, the GI organ classification AI model demonstrated high accuracy on CE videos. The transitional area could be more easily located by adjusting the cut-off of the AI score and visualization of its result over time.
Collapse
Affiliation(s)
- Joowon Chung
- Department of Internal Medicine, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul 01830, Republic of Korea
| | - Dong Jun Oh
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea
| | - Junseok Park
- Department of Internal Medicine, Digestive Disease Center, Institute for Digestive Research, Soonchunhyang University College of Medicine, Seoul 04401, Republic of Korea
| | - Su Hwan Kim
- Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul 07061, Republic of Korea
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea
| |
Collapse
|
|
15
|
Parkash O, Siddiqui ATS, Jiwani U, Rind F, Padhani ZA, Rizvi A, Hoodbhoy Z, Das JK. Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis. Front Med (Lausanne) 2022; 9:1018937. [PMID: 36405592 PMCID: PMC9672666 DOI: 10.3389/fmed.2022.1018937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/03/2022] [Indexed: 11/06/2022] Open
Abstract
Background Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms, and inflammatory bowel disease. Methods We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify studies assessing the diagnostic performance of AI models for GI luminal pathologies. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. We performed a meta-analysis and hierarchical summary receiver operating characteristic curves (HSROC). The risk of bias was assessed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Subgroup analyses were conducted based on the type of GI luminal disease, AI model, reference standard, and type of data used for analysis. This study is registered with PROSPERO (CRD42021288360). Findings We included 73 studies, of which 31 were externally validated and provided sufficient information for inclusion in the meta-analysis. The overall sensitivity of AI for detecting GI luminal pathologies was 91.9% (95% CI: 89.0–94.1) and specificity was 91.7% (95% CI: 87.4–94.7). Deep learning models (sensitivity: 89.8%, specificity: 91.9%) and ensemble methods (sensitivity: 95.4%, specificity: 90.9%) were the most commonly used models in the included studies. Majority of studies (n = 56, 76.7%) had a high risk of selection bias while 74% (n = 54) studies were low risk on reference standard and 67% (n = 49) were low risk for flow and timing bias. Interpretation The review suggests high sensitivity and specificity of AI models for the detection of GI luminal pathologies. There is a need for large, multi-center trials in both high income countries and low- and middle- income countries to assess the performance of these AI models in real clinical settings and its impact on diagnosis and prognosis. Systematic review registration [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=288360], identifier [CRD42021288360].
Collapse
Affiliation(s)
- Om Parkash
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | | | - Uswa Jiwani
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Fahad Rind
- Head and Neck Oncology, The Ohio State University, Columbus, OH, United States
| | - Zahra Ali Padhani
- Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
| | - Arjumand Rizvi
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Zahra Hoodbhoy
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Jai K. Das
- Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
- *Correspondence: Jai K. Das,
| |
Collapse
|
|
16
|
Differentiation of Urothelial Carcinoma in Histopathology Images Using Deep Learning and Visualisation. J Pathol Inform 2022; 14:100155. [DOI: 10.1016/j.jpi.2022.100155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/16/2022] [Accepted: 11/02/2022] [Indexed: 11/09/2022] Open
|
|
17
|
Chetcuti Zammit S, Sidhu R. Artificial intelligence within the small bowel: are we lagging behind? Curr Opin Gastroenterol 2022; 38:307-317. [PMID: 35645023 DOI: 10.1097/mog.0000000000000827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
PURPOSE OF REVIEW The use of artificial intelligence in small bowel capsule endoscopy is expanding. This review focusses on the use of artificial intelligence for small bowel pathology compared with human data and developments to date. RECENT FINDINGS The diagnosis and management of small bowel disease has been revolutionized with the advent of capsule endoscopy. Reading of capsule endoscopy videos however is time consuming with an average reading time of 40 min. Furthermore, the fatigued human eye may miss subtle lesions including indiscreet mucosal bulges. In recent years, artificial intelligence has made significant progress in the field of medicine including gastroenterology. Machine learning has enabled feature extraction and in combination with deep neural networks, image classification has now materialized for routine endoscopy for the clinician. SUMMARY Artificial intelligence is in built within the Navicam-Ankon capsule endoscopy reading system. This development will no doubt expand to other capsule endoscopy platforms and capsule endoscopies that are used to visualize other parts of the gastrointestinal tract as a standard. This wireless and patient friendly technique combined with rapid reading platforms with the help of artificial intelligence will become an attractive and viable choice to alter how patients are investigated in the future.
Collapse
Affiliation(s)
| | - Reena Sidhu
- Academic Department of Gastroenterology, Royal Hallamshire Hospital
- Academic Unit of Gastroenterology, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| |
Collapse
|
|
18
|
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.
Collapse
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
| |
Collapse
|
|
19
|
Oh CK, Kim T, Cho YK, Cheung DY, Lee BI, Cho YS, Kim JI, Choi MG, Lee HH, Lee S. Convolutional neural network-based object detection model to identify gastrointestinal stromal tumors in endoscopic ultrasound images. J Gastroenterol Hepatol 2021; 36:3387-3394. [PMID: 34369001 DOI: 10.1111/jgh.15653] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/27/2021] [Accepted: 08/02/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND AND AIM We aimed to develop a convolutional neural network (CNN)-based object detection model for the discrimination of gastric subepithelial tumors, such as gastrointestinal stromal tumors (GISTs), and leiomyomas, in endoscopic ultrasound (EUS) images. METHODS We used 376 images from 114 patients with histologically confirmed gastric GIST or leiomyoma to train the EUS-CNN. We constructed the EUS-CNN using an EfficientNet CNN model for feature extraction and a weighted bi-directional feature pyramid network for object detection. We assessed the performance of our EUS-CNN by calculating its accuracy, sensitivity, specificity, and area under receiver operating characteristic curve (AUC) using a validation set of 170 images from 54 patients. Four EUS experts and 15 EUS trainees were asked to judge the same validation dataset, and the diagnostic yields were compared between the EUS-CNN and human assessments. RESULTS In the per-image analysis, the sensitivity, specificity, accuracy, and AUC of our EUS-CNN were 95.6%, 82.1%, 91.2%, and 0.9234, respectively. In the per-patient analysis, the sensitivity, specificity, accuracy, and AUC for our object detection model were 100.0%, 85.7%, 96.3%, and 0.9929, respectively. The EUS-CNN outperformed human assessment in terms of accuracy, sensitivity, and negative predictive value. CONCLUSIONS We developed the EUS-CNN system, which demonstrated high diagnostic ability for gastric GIST prediction. This EUS-CNN system can be helpful not only for less-experienced endoscopists but also for experienced ones. Additional EUS image accumulation and prospective studies are required alongside validation in a large multicenter trial.
Collapse
Affiliation(s)
- Chang Kyo Oh
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Taewan Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea.,Postech-Catholic Biomedical Engineering Institute, Pohang University of Science and Technology (POSTECH), Pohang, South Korea
| | - Yu Kyung Cho
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Dae Young Cheung
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Bo-In Lee
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Young-Seok Cho
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Jin Il Kim
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Myung-Gyu Choi
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Han Hee Lee
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea.,Postech-Catholic Biomedical Engineering Institute, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Seungchul Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea.,Postech-Catholic Biomedical Engineering Institute, Pohang University of Science and Technology (POSTECH), Pohang, South Korea.,Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), Pohang, South Korea.,Institute of Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, South Korea
| |
Collapse
|
|
20
|
Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27:6191-6223. [PMID: 34712027 PMCID: PMC8515803 DOI: 10.3748/wjg.v27.i37.6191] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/06/2021] [Accepted: 08/31/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.
Collapse
Affiliation(s)
- Chrysanthos D Christou
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| |
Collapse
|
|
21
|
Cox II GA, Jackson CS, Vega KJ. Artificial intelligence as a means to improve recognition of gastrointestinal angiodysplasia in video capsule endoscopy. Artif Intell Gastrointest Endosc 2021; 2:179-184. [DOI: 10.37126/aige.v2.i4.179] [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: 06/03/2021] [Revised: 07/07/2021] [Accepted: 08/13/2021] [Indexed: 02/06/2023] Open
Abstract
Gastrointestinal angiodysplasia (GIAD) is defined as the pathological process where blood vessels, typically venules and capillaries, become engorged, tortuous and thin walled – which then form arteriovenous connections within the mucosal and submucosal layers of the gastrointestinal tract. GIADs are a significant cause of gastrointestinal bleeding and are the main cause for suspected small bowel bleeding. To make the diagnosis, gastroenterologists rely on the use of video capsule endoscopy (VCE) to “target” GIAD. However, the use of VCE can be cumbersome secondary to reader fatigue, suboptimal preparation, and difficulty in distinguishing images. The human eye is imperfect. The same capsule study read by two different readers are noted to have miss rates like other forms of endoscopy. Artificial intelligence (AI) has been a means to bridge the gap between human imperfection and recognition of GIAD. The use of AI in VCE have shown that detection has improved, however the other burdens and limitations still need to be addressed. The use of AI for the diagnosis of GIAD shows promise and the changes needed to enhance the current practice of VCE are near.
Collapse
Affiliation(s)
- Gerald A Cox II
- Department of Medicine, Loma Linda University Medical Center, Loma Linda, CA 92354, United States
| | - Christian S Jackson
- Gastroenterology Section, VA Loma Linda Healthcare System, Loma Linda, CA 92357, United States
| | - Kenneth J Vega
- Division of Gastroenterology and Hepatology, Augusta University - Medical College of Georgia, Augusta, GA 30912, United States
| |
Collapse
|
|
22
|
Kong Z, He M, Luo Q, Huang X, Wei P, Cheng Y, Chen L, Liang Y, Lu Y, Li X, Chen J. Multi-Task Classification and Segmentation for Explicable Capsule Endoscopy Diagnostics. Front Mol Biosci 2021; 8:614277. [PMID: 34490342 PMCID: PMC8417442 DOI: 10.3389/fmolb.2021.614277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 07/23/2021] [Indexed: 01/22/2023] Open
Abstract
Capsule endoscopy is a leading diagnostic tool for small bowel lesions which faces certain challenges such as time-consuming interpretation and harsh optical environment inside the small intestine. Specialists unavoidably waste lots of time on searching for a high clearness degree image for accurate diagnostics. However, current clearness degree classification methods are based on either traditional attributes or an unexplainable deep neural network. In this paper, we propose a multi-task framework, called the multi-task classification and segmentation network (MTCSN), to achieve joint learning of clearness degree (CD) and tissue semantic segmentation (TSS) for the first time. In the MTCSN, the CD helps to generate better refined TSS, while TSS provides an explicable semantic map to better classify the CD. In addition, we present a new benchmark, named the Capsule-Endoscopy Crohn's Disease dataset, which introduces the challenges faced in the real world including motion blur, excreta occlusion, reflection, and various complex alimentary scenes that are widely acknowledged in endoscopy examination. Extensive experiments and ablation studies report the significant performance gains of the MTCSN over state-of-the-art methods.
Collapse
Affiliation(s)
- Zishang Kong
- School of Electonic and Computer Engineering, Peking University, Shenzhen, China
| | - Min He
- Department of Gastroenterology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Qianjiang Luo
- Department of Gastroenterology, Peking University Shenzhen Hospital, Shenzhen, China
| | | | - Pengxu Wei
- Peng Cheng Laboratory, Shenzhen, China
- Sun Yat-sen University, Guangzhou, China
| | - Yalu Cheng
- School of Electonic and Computer Engineering, Peking University, Shenzhen, China
| | - Luyang Chen
- Pennsylvania State University, Philadelphia, PA, United States
| | | | - Yanchang Lu
- Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China
| | - Xi Li
- Department of Gastroenterology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Jie Chen
- School of Electonic and Computer Engineering, Peking University, Shenzhen, China
- Peng Cheng Laboratory, Shenzhen, China
| |
Collapse
|
|
23
|
Chen L, Li DC. Artificial intelligence and inflammatory bowel disease. Shijie Huaren Xiaohua Zazhi 2021; 29:684-689. [DOI: 10.11569/wcjd.v29.i13.684] [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
With the development of artificial intelligence (AI) and its gradual application in the medical field, AI has brought new ideas to the medical development. The research and application of AI in inflammatory l bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn's disease (CD), are increasing. Selecting appropriate models and methods through machine learning can help diagnose, treat, and predict the prognosis of IBD. In recent years, AI combined with endoscopy has made an appearance in the diagnosis of IBD and achieved satisfactory results. At the same time, AI plays an important role in the process of disease prediction and treatment evaluation for patients with IBD. However, we should also be aware that there are still some problems with AI. This paper gives a brief review of the practical application value of AI in IBD.
Collapse
Affiliation(s)
- Lei Chen
- Graduate School of Bengbu Medical College, Bengbu 233030, Anhui Province, China
| | - De-Chun Li
- Department of Radiology, Xuzhou Central Hospital Affiliated to Medical School of Southeast University, Xuzhou 221009, Jiangsu Province, China
| |
Collapse
|
|
24
|
Yang Y, Li YX, Yao RQ, Du XH, Ren C. Artificial intelligence in small intestinal diseases: Application and prospects. World J Gastroenterol 2021; 27:3734-3747. [PMID: 34321840 PMCID: PMC8291013 DOI: 10.3748/wjg.v27.i25.3734] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/09/2021] [Accepted: 05/08/2021] [Indexed: 02/06/2023] Open
Abstract
The small intestine is located in the middle of the gastrointestinal tract, so small intestinal diseases are more difficult to diagnose than other gastrointestinal diseases. However, with the extensive application of artificial intelligence in the field of small intestinal diseases, with its efficient learning capacities and computational power, artificial intelligence plays an important role in the auxiliary diagnosis and prognosis prediction based on the capsule endoscopy and other examination methods, which improves the accuracy of diagnosis and prediction and reduces the workload of doctors. In this review, a comprehensive retrieval was performed on articles published up to October 2020 from PubMed and other databases. Thereby the application status of artificial intelligence in small intestinal diseases was systematically introduced, and the challenges and prospects in this field were also analyzed.
Collapse
Affiliation(s)
- Yu Yang
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Yu-Xuan Li
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Ren-Qi Yao
- Trauma Research Center, The Fourth Medical Center and Medical Innovation Research Division of the Chinese People‘s Liberation Army General Hospital, Beijing 100048, China
- Department of Burn Surgery, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Xiao-Hui Du
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Chao Ren
- Trauma Research Center, The Fourth Medical Center and Medical Innovation Research Division of the Chinese People‘s Liberation Army General Hospital, Beijing 100048, China
| |
Collapse
|
|
25
|
Cortegoso Valdivia P, Elosua A, Houdeville C, Pennazio M, Fernández-Urién I, Dray X, Toth E, Eliakim R, Koulaouzidis A. Clinical feasibility of panintestinal (or panenteric) capsule endoscopy: a systematic review. Eur J Gastroenterol Hepatol 2021; 33:949-955. [PMID: 34034282 DOI: 10.1097/meg.0000000000002200] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In recent years, panintestinal capsule endoscopy (PCE) with double-headed capsules has been used to perform complete, single-sitting exploration of both small bowel and colon in different clinical conditions. Double-headed capsules for colonic examination (CCE) have been exploited first in this setting, followed by newer generations of capsules (i.e. PillCam Crohn, PCC) specifically engineered for this purpose. The aim of this study was to evaluate the feasibility of PCE in the form of a systematic review. We performed a comprehensive literature search to identify papers in which CE was specifically used for a PCE of the gastrointestinal tract. Data on CE, bowel preparation regimen, rate of cleanliness and completeness, and data on transit times were analyzed. The primary outcome was to assess the feasibility of a whole-gut exploration with CE. Sixteen (n = 16) studies including 915 CE procedures with CCE1 (n = 134), CCE2 (n = 357) and PCC (n = 424) were included. 13/16 studies were performed in the setting of Crohn's disease. Cleanliness and completeness rates were acceptable in all studies, ranging from 63.9% and 68.6% to 100%, respectively. In conclusion, PCE is a feasible technique, although further structured studies are needed to explore its full potential.
Collapse
Affiliation(s)
- Pablo Cortegoso Valdivia
- Gastroenterology and Endoscopy Unit, University Hospital of Parma, University of Parma, Parma, Italy
| | - Alfonso Elosua
- Gastroenterology Unit, Hospital Garcia Orcoyen, Estella, Spain
| | - Charles Houdeville
- Sorbonne Université, Centre d'Endoscopie Digestive, Hôpital Saint-Antoine, APHP, Paris, France
| | - Marco Pennazio
- University Division of Gastroenterology, AOU Città della Salute e della Scienza, University of Turin, Turin, Italy
| | | | - Xavier Dray
- Sorbonne Université, Centre d'Endoscopie Digestive, Hôpital Saint-Antoine, APHP, Paris, France
| | - Ervin Toth
- Department of Gastroenterology, Skåne University Hospital, Lund University, Malmö, Sweden
| | - Rami Eliakim
- Department of Gastroenterology, Chaim Sheba Medical Center, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Anastasios Koulaouzidis
- Department of Social Medicine & Public Health, Pomeranian Medical University, Szczecin, Poland
| |
Collapse
|
|
26
|
A Current and Newly Proposed Artificial Intelligence Algorithm for Reading Small Bowel Capsule Endoscopy. Diagnostics (Basel) 2021; 11:diagnostics11071183. [PMID: 34209948 PMCID: PMC8306692 DOI: 10.3390/diagnostics11071183] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/26/2021] [Accepted: 06/28/2021] [Indexed: 12/09/2022] Open
Abstract
Small bowel capsule endoscopy (SBCE) is one of the most useful methods for diagnosing small bowel mucosal lesions. However, it takes a long time to interpret the capsule images. To solve this problem, artificial intelligence (AI) algorithms for SBCE readings are being actively studied. In this article, we analyzed several studies that applied AI algorithms to SBCE readings, such as automatic lesion detection, automatic classification of bowel cleanliness, and automatic compartmentalization of small bowels. In addition to automatic lesion detection using AI algorithms, a new direction of AI algorithms related to shorter reading times and improved lesion detection accuracy should be considered. Therefore, it is necessary to develop an integrated AI algorithm composed of algorithms with various functions in order to be used in clinical practice.
Collapse
|
|
27
|
Cao B, Zhang KC, Wei B, Chen L. Status quo and future prospects of artificial neural network from the perspective of gastroenterologists. World J Gastroenterol 2021; 27:2681-2709. [PMID: 34135549 PMCID: PMC8173384 DOI: 10.3748/wjg.v27.i21.2681] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/29/2021] [Accepted: 04/22/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.
Collapse
Affiliation(s)
- Bo Cao
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Ke-Cheng Zhang
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Bo Wei
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Lin Chen
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| |
Collapse
|
|
28
|
Yang H, Hu B. Application of artificial intelligence to endoscopy on common gastrointestinal benign diseases. Artif Intell Gastrointest Endosc 2021; 2:25-35. [DOI: 10.37126/aige.v2.i2.25] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 03/17/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) has been widely involved in every aspect of healthcare in the preclinical stage. In the digestive system, AI has been trained to assist auxiliary examinations including histopathology, endoscopy, ultrasonography, computerized tomography, and magnetic resonance imaging in detection, diagnosis, classification, differentiation, prognosis, and quality control. In the field of endoscopy, the application of AI, such as automatic detection, diagnosis, classification, and invasion depth, in early gastrointestinal (GI) cancers has received wide attention. There is a paucity of studies of AI application on common GI benign diseases based on endoscopy. In the review, we provide an overview of AI applications to endoscopy on common GI benign diseases including in the esophagus, stomach, intestine, and colon. It indicates that AI will gradually become an indispensable part of normal endoscopic detection and diagnosis of common GI benign diseases as clinical data, algorithms, and other related work are constantly repeated and improved.
Collapse
Affiliation(s)
- Hang Yang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| |
Collapse
|
|
29
|
Trasolini R, Byrne MF. Artificial intelligence and deep learning for small bowel capsule endoscopy. Dig Endosc 2021; 33:290-297. [PMID: 33211357 DOI: 10.1111/den.13896] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 11/16/2020] [Indexed: 12/20/2022]
Abstract
Capsule endoscopy is ideally suited to artificial intelligence-based interpretation given its reliance on pattern recognition in still images. Time saving viewing modes and lesion detection features currently available rely on machine learning algorithms, a form of artificial intelligence. Current software necessitates close human supervision given poor sensitivity relative to an expert reader. However, with the advent of deep learning, artificial intelligence is becoming increasingly reliable and will be increasingly relied upon. We review the major advances in artificial intelligence for capsule endoscopy in recent publications and briefly review artificial intelligence development for historical understanding. Importantly, recent advancements in artificial intelligence have not yet been incorporated into practice and it is immature to judge the potential of this technology based on current platforms. Remaining regulatory and standardization hurdles are being overcome and artificial intelligence-based clinical applications are likely to proliferate rapidly.
Collapse
Affiliation(s)
- Roberto Trasolini
- Department of Medicine, The University of British Columbia, Vancouver, Canada
| | - Michael F Byrne
- Department of Medicine, The University of British Columbia, Vancouver, Canada
| |
Collapse
|
|
30
|
Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci 2021; 41:1105-1115. [PMID: 34874486 PMCID: PMC8648557 DOI: 10.1007/s11596-021-2474-3] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 12/01/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) is a new technical discipline that uses computer technology to research and develop the theory, method, technique, and application system for the simulation, extension, and expansion of human intelligence. With the assistance of new AI technology, the traditional medical environment has changed a lot. For example, a patient's diagnosis based on radiological, pathological, endoscopic, ultrasonographic, and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload. The medical treatments during the perioperative period, including the preoperative preparation, surgical period, and postoperative recovery period, have been significantly enhanced with better surgical effects. In addition, AI technology has also played a crucial role in medical drug production, medical management, and medical education, taking them into a new direction. The purpose of this review is to introduce the application of AI in medicine and to provide an outlook of future trends.
Collapse
Affiliation(s)
- Peng-ran Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Lin Lu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Jia-yao Zhang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Tong-tong Huo
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Song-xiang Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Zhe-wei Ye
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| |
Collapse
|