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Martinez M, Bartel MJ, Chua T, Dakhoul L, Fatima H, Jensen D, Lara LF, Tadros M, Villa E, Yang D, Saltzman JR. The 2023 top 10 list of endoscopy topics in medical publishing: an annual review by the American Society for Gastrointestinal Endoscopy Editorial Board. Gastrointest Endosc 2024; 100:537-548. [PMID: 38729314 DOI: 10.1016/j.gie.2024.05.002] [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] [Received: 04/21/2024] [Accepted: 05/01/2024] [Indexed: 05/12/2024]
Abstract
Using a systematic literature search of original articles published during 2023 in Gastrointestinal Endoscopy (GIE) and other high-impact medical and gastroenterology journals, the GIE Editorial Board of the American Society for Gastrointestinal Endoscopy compiled a list of the top 10 most significant topic areas in general and advanced GI endoscopy during the year. Each GIE Editorial Board member was directed to consider 3 criteria in generating candidate topics-significance, novelty, and impact on global clinical practice-and subject matter consensus was facilitated by the Chair through electronic voting and a meeting of the entire GIE Editorial Board. The 10 identified areas collectively represent advances in the following endoscopic spheres: GI bleeding, endohepatology, endoscopic palliation, artificial intelligence and polyp detection, artificial intelligence beyond the colon, better polypectomy and EMR, how to make endoscopy units greener, high-quality upper endoscopy, endoscopic tissue apposition and closure devices, and endoscopic submucosal dissection. Each board member was assigned a topic area around which to summarize relevant important articles, thereby generating this overview of the "top 10" endoscopic advances of 2023.
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Affiliation(s)
- Melissa Martinez
- Digestive Health Institute, Carle Foundation Hospital, Urbana, Illinois, USA
| | | | - Tiffany Chua
- Department of Gastroenterology, Hepatology and Nutrition, University of Florida, Gainesville, Florida, USA
| | - Lara Dakhoul
- Department of Gastroenterology, Hepatology and Nutrition, University of Florida, Gainesville, Florida, USA
| | - Hala Fatima
- Department of Internal Medicine, Division of Gastroenterology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Dennis Jensen
- Ronald Reagan UCLA Medical Center and The VA Greater Los Angeles Healthcare System, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Luis F Lara
- Division of Digestive Diseases, University of Cincinnati, Cincinnati, Ohio, USA
| | - Michael Tadros
- Division of Gastroenterology, Albany Medical Center, Albany, New York, USA
| | | | - Dennis Yang
- Center of Interventional Endoscopy, Advent Health, Orlando, Florida, USA
| | - John R Saltzman
- Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
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Bou Jaoude J, Al Bacha R, Abboud B. Will artificial intelligence reach any limit in gastroenterology? Artif Intell Gastroenterol 2024; 5:91336. [DOI: 10.35712/aig.v5.i2.91336] [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: 12/27/2023] [Revised: 04/25/2024] [Accepted: 06/07/2024] [Indexed: 08/08/2024] Open
Abstract
Endoscopy is the cornerstone in the management of digestive diseases. Over the last few decades, technology has played an important role in the development of this field, helping endoscopists in better detecting and characterizing luminal lesions. However, despite ongoing advancements in endoscopic technology, the incidence of missed pre-neoplastic and neoplastic lesions remains high due to the operator-dependent nature of endoscopy and the challenging learning curve associated with new technologies. Artificial intelligence (AI), an operator-independent field, could be an invaluable solution. AI can serve as a “second observer”, enhancing the performance of endoscopists in detecting and characterizing luminal lesions. By utilizing deep learning (DL), an innovation within machine learning, AI automatically extracts input features from targeted endoscopic images. DL encompasses both computer-aided detection and computer-aided diagnosis, assisting endoscopists in reducing missed detection rates and predicting the histology of luminal digestive lesions. AI applications in clinical gastrointestinal diseases are continuously expanding and evolving the entire digestive tract. In all published studies, real-time AI assists endoscopists in improving the performance of non-expert gastroenterologists, bringing it to a level comparable to that of experts. The development of DL may be affected by selection biases. Studies have utilized different AI-assisted models, which are heterogeneous. In the future, algorithms need validation through large, randomized trials. Theoretically, AI has no limit to assist endoscopists in increasing the accuracy and the quality of endoscopic exams. However, practically, we still have a long way to go before standardizing our AI models to be accepted and applied by all gastroenterologists.
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Affiliation(s)
- Joseph Bou Jaoude
- Department of Gastroenterology, Levant Hospital, Beirut 166830, Lebanon
| | - Rose Al Bacha
- Department of Gastroenterology, Levant Hospital, Beirut 166830, Lebanon
| | - Bassam Abboud
- Department of General Surgery, Geitaoui Hospital, Faculty of Medicine, Lebanese University, Lebanon, Beirut 166830, Lebanon
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Rex DK, Bhavsar-Burke I, Buckles D, Burton J, Cartee A, Comar K, Edwards A, Fennimore B, Fischer M, Gerich M, Gilmore A, Hamdeh S, Hoffman J, Ibach M, Jackson M, James-Stevenson T, Kaltenbach T, Kaplan J, Kapur S, Kohm D, Kriss M, Kundumadam S, Kyanam Kabir Baig KR, Menard-Katcher P, Kraft C, Langworthy J, Misra B, Molloy E, Munoz JC, Norvell J, Nowak T, Obaitan I, Patel S, Patel M, Peter S, Reid BM, Rogers N, Ross J, Ryan J, Sagi S, Saito A, Samo S, Sarkis F, Scott FI, Siwiec R, Sullivan S, Wieland A, Zhang J, Repici A, Hassan C, Byrne MF, Rastogi A. Artificial Intelligence for Real-Time Prediction of the Histology of Colorectal Polyps by General Endoscopists. Ann Intern Med 2024; 177:911-918. [PMID: 38768450 DOI: 10.7326/m24-0086] [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: 05/22/2024] Open
Abstract
BACKGROUND Real-time prediction of histologic features of small colorectal polyps may prevent resection and/or pathologic evaluation and therefore decrease colonoscopy costs. Previous studies showed that computer-aided diagnosis (CADx) was highly accurate, though it did not outperform expert endoscopists. OBJECTIVE To assess the diagnostic performance of histologic predictions by general endoscopists before and after assistance from CADx in a real-life setting. DESIGN Prospective, multicenter, single-group study. (ClinicalTrials.gov: NCT04437615). SETTING 6 centers across the United States. PARTICIPANTS 1252 consecutive patients undergoing colonoscopy and 49 general endoscopists with variable experience in real-time prediction of polyp histologic features. INTERVENTION Real-time use of CADx during routine colonoscopy. MEASUREMENTS The primary end points were the sensitivity and specificity of CADx-unassisted and CADx-assisted histologic predictions for adenomas measuring 5 mm or less. For clinical purposes, additional estimates according to location and confidence level were provided. RESULTS The CADx device made a diagnosis for 2695 polyps measuring 5 mm or less (96%) in 1252 patients. There was no difference in sensitivity between the unassisted and assisted groups (90.7% vs. 90.8%; P = 0.52). Specificity was higher in the CADx-assisted group (59.5% vs. 64.7%; P < 0.001). Among all 2695 polyps measuring 5 mm or less, 88.2% and 86.1% (P < 0.001) in the CADx-assisted and unassisted groups, respectively, could be resected and discarded without pathologic evaluation. Among 743 rectosigmoid polyps measuring 5 mm or less, 49.5% and 47.9% (P < 0.001) in the CADx-assisted and unassisted groups, respectively, could be left in situ without resection. LIMITATION Decision making based on CADx might differ outside a clinical trial. CONCLUSION CADx assistance did not result in increased sensitivity of optical diagnosis. Despite a slight increase, the specificity of CADx-assisted diagnosis remained suboptimal. PRIMARY FUNDING SOURCE Olympus America Corporation served as the clinical study sponsor.
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Affiliation(s)
- Douglas K Rex
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Indira Bhavsar-Burke
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Daniel Buckles
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - James Burton
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Amanda Cartee
- University of Alabama at Birmingham, Birmingham, Alabama (A.C., A.E., K.R.K.K.B., S.P., F.S.)
| | - Kevin Comar
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - Adam Edwards
- University of Alabama at Birmingham, Birmingham, Alabama (A.C., A.E., K.R.K.K.B., S.P., F.S.)
| | - Blair Fennimore
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Monika Fischer
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Mark Gerich
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Ashley Gilmore
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Shadi Hamdeh
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - Jeffrey Hoffman
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - Michael Ibach
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - Mollie Jackson
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - Toyia James-Stevenson
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Tonya Kaltenbach
- San Francisco VA Medical Center, San Francisco, California (T.K., C.K., J.R.)
| | - Jeffrey Kaplan
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Saurabh Kapur
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - Daniel Kohm
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - Michael Kriss
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Shanker Kundumadam
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | | | - Paul Menard-Katcher
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Cary Kraft
- San Francisco VA Medical Center, San Francisco, California (T.K., C.K., J.R.)
| | - James Langworthy
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - Bharat Misra
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - Eric Molloy
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - Juan Carlos Munoz
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - John Norvell
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Thomas Nowak
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Itegbemie Obaitan
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Swati Patel
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Mitesh Patel
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - Shajan Peter
- University of Alabama at Birmingham, Birmingham, Alabama (A.C., A.E., K.R.K.K.B., S.P., F.S.)
| | - B Marie Reid
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - Nicholas Rogers
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Jason Ross
- Borland Groover, Jacksonville, Florida (K.C., J.H., M.I., D.K., B.M., J.C.M., B.M.R., J.R.)
| | - James Ryan
- San Francisco VA Medical Center, San Francisco, California (T.K., C.K., J.R.)
| | - Sashidhar Sagi
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Akira Saito
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Salih Samo
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
| | - Fayez Sarkis
- University of Alabama at Birmingham, Birmingham, Alabama (A.C., A.E., K.R.K.K.B., S.P., F.S.)
| | - Frank I Scott
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Robert Siwiec
- Indiana University School of Medicine, Indianapolis, Indiana (D.K.R., I.B., M.F., A.G., T.J., S.K., T.N., I.O., N.R., S.S., A.S., R.S.)
| | - Shelby Sullivan
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Amanda Wieland
- University of Colorado Boulder, Boulder, Colorado (J.B., B.F., M.G., J.K., M.K., P.M., J.N., S.P., F.S., S.S., A.W.)
| | - Jianying Zhang
- Department of Statistics, Olympus America Corporation, Center Valley, Pennsylvania (J.Z.)
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy, and Endoscopy Unit and Humanitas Clinical and Research Hospital, IRCCS, Rozzano, Italy (A.R., C.H.)
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy, and Endoscopy Unit and Humanitas Clinical and Research Hospital, IRCCS, Rozzano, Italy (A.R., C.H.)
| | - Michael F Byrne
- Division of Gastroenterology, Vancouver General Hospital; University of British Columbia; and Satisfai Health, Vancouver, British Columbia, Canada (M.F.B.)
| | - Amit Rastogi
- University of Kansas Medical Center, Kansas City, Kansas (D.B., S.H., M.J., S.K., J.L., E.M., M.P., S.S., A.R.)
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Mori Y, Jin EH, Lee D. Enhancing artificial intelligence-doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy. Dig Liver Dis 2024; 56:1140-1143. [PMID: 38105144 DOI: 10.1016/j.dld.2023.11.033] [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: 08/18/2023] [Revised: 10/16/2023] [Accepted: 11/22/2023] [Indexed: 12/19/2023]
Abstract
Establishing appropriate trust and maintaining a balanced reliance on digital resources are vital for accurate optical diagnoses and effective integration of computer-aided diagnosis (CADx) in colonoscopy. Active learning using diverse polyp image datasets can help in developing precise CADx systems. Enhancing doctors' digital literacy and interpreting their results is crucial. Explainable artificial intelligence (AI) addresses opacity, and textual descriptions, along with AI-generated content, deepen the interpretability of AI-based findings by doctors. AI conveying uncertainties and decision confidence aids doctors' acceptance of results. Optimal AI-doctor collaboration requires improving algorithm performance, transparency, addressing uncertainties, and enhancing doctors' optical diagnostic skills.
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Affiliation(s)
- Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo and Oslo University Hospital, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Eun Hyo Jin
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea; Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, South Korea.
| | - Dongheon Lee
- Department of Biomedical Engineering, College of Medicine, Chungnam National University, Daejeon, South Korea; Department of Biomedical Engineering, Chungnam National University Hospital, Daejeon, South Korea
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Spadaccini M, Troya J, Khalaf K, Facciorusso A, Maselli R, Hann A, Repici A. Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going? Dig Liver Dis 2024; 56:1148-1155. [PMID: 38458884 DOI: 10.1016/j.dld.2024.01.203] [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: 10/12/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/10/2024]
Abstract
Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.
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Affiliation(s)
- Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy.
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Surgical and Medical Sciences, University of Foggia, Foggia, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
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Mandarino FV, Danese S, Uraoka T, Parra-Blanco A, Maeda Y, Saito Y, Kudo SE, Bourke MJ, Iacucci M. Precision endoscopy in colorectal polyps' characterization and planning of endoscopic therapy. Dig Endosc 2024; 36:761-777. [PMID: 37988279 DOI: 10.1111/den.14727] [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: 08/25/2023] [Accepted: 11/19/2023] [Indexed: 11/23/2023]
Abstract
Precision endoscopy in the management of colorectal polyps and early colorectal cancer has emerged as the standard of care. It includes optical characterization of polyps and estimation of submucosal invasion depth of large nonpedunculated colorectal polyps to select the appropriate endoscopic resection modality. Over time, several imaging modalities have been implemented in endoscopic practice to improve optical performance. Among these, image-enhanced endoscopy systems and magnification endoscopy represent now well-established tools. New advanced technologies, such as endocytoscopy and confocal laser endomicroscopy, have recently shown promising results in predicting the histology of colorectal polyps. In recent years, artificial intelligence has continued to enhance endoscopic performance in the characterization of colorectal polyps, overcoming the limitations of other imaging modes. In this review we retrace the path of precision endoscopy, analyzing the yield of various endoscopic imaging techniques in personalizing management of colorectal polyps and early colorectal cancer.
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Affiliation(s)
- Francesco Vito Mandarino
- Department of Gastroenterology and Gastrointestinal Endoscopy, San Raffaele Hospital IRCSS, Milan, Italy
- Department of Gastrointestinal Endoscopy, Westmead Hospital, Sydney, NSW, Australia
| | - Silvio Danese
- Department of Gastroenterology and Gastrointestinal Endoscopy, San Raffaele Hospital IRCSS, Milan, Italy
| | - Toshio Uraoka
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine, Gumma, Japan
| | - Adolfo Parra-Blanco
- NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK
| | - Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Michael J Bourke
- Department of Gastrointestinal Endoscopy, Westmead Hospital, Sydney, NSW, Australia
| | - Marietta Iacucci
- Department of Gastroenterology, University College Cork, Cork, Ireland
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Hassan C, Misawa M, Rizkala T, Mori Y, Sultan S, Facciorusso A, Antonelli G, Spadaccini M, Houwen BBSL, Rondonotti E, Patel H, Khalaf K, Li JW, Fernandez GM, Bhandari P, Dekker E, Gross S, Berzin T, Vandvik PO, Correale L, Kudo SE, Sharma P, Rex DK, Repici A, Foroutan F. Computer-Aided Diagnosis for Leaving Colorectal Polyps In Situ : A Systematic Review and Meta-analysis. Ann Intern Med 2024; 177:919-928. [PMID: 38768453 DOI: 10.7326/m23-2865] [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: 05/22/2024] Open
Abstract
BACKGROUND Computer-aided diagnosis (CADx) allows prediction of polyp histology during colonoscopy, which may reduce unnecessary removal of nonneoplastic polyps. However, the potential benefits and harms of CADx are still unclear. PURPOSE To quantify the benefit and harm of using CADx in colonoscopy for the optical diagnosis of small (≤5-mm) rectosigmoid polyps. DATA SOURCES Medline, Embase, and Scopus were searched for articles published before 22 December 2023. STUDY SELECTION Histologically verified diagnostic accuracy studies that evaluated the real-time performance of physicians in predicting neoplastic change of small rectosigmoid polyps without or with CADx assistance during colonoscopy. DATA EXTRACTION The clinical benefit and harm were estimated on the basis of accuracy values of the endoscopist before and after CADx assistance. The certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework. The outcome measure for benefit was the proportion of polyps predicted to be nonneoplastic that would avoid removal with the use of CADx. The outcome measure for harm was the proportion of neoplastic polyps that would be not resected and left in situ due to an incorrect diagnosis with the use of CADx. Histology served as the reference standard for both outcomes. DATA SYNTHESIS Ten studies, including 3620 patients with 4103 small rectosigmoid polyps, were analyzed. The studies that assessed the performance of CADx alone (9 studies; 3237 polyps) showed a sensitivity of 87.3% (95% CI, 79.2% to 92.5%) and specificity of 88.9% (CI, 81.7% to 93.5%) in predicting neoplastic change. In the studies that compared histology prediction performance before versus after CADx assistance (4 studies; 2503 polyps), there was no difference in the proportion of polyps predicted to be nonneoplastic that would avoid removal (55.4% vs. 58.4%; risk ratio [RR], 1.06 [CI, 0.96 to 1.17]; moderate-certainty evidence) or in the proportion of neoplastic polyps that would be erroneously left in situ (8.2% vs. 7.5%; RR, 0.95 [CI, 0.69 to 1.33]; moderate-certainty evidence). LIMITATION The application of optical diagnosis was only simulated, potentially altering the decision-making process of the operator. CONCLUSION Computer-aided diagnosis provided no incremental benefit or harm in the management of small rectosigmoid polyps during colonoscopy. PRIMARY FUNDING SOURCE European Commission. (PROSPERO: CRD42023402197).
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Affiliation(s)
- Cesare Hassan
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, and Humanitas Clinical and Research Center IRCCS, Endoscopy Unit, Rozzano, Italy (C.H., M.S., A.R.)
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan (M.M., S.K.)
| | - Tommy Rizkala
- Humanitas Clinical and Research Center IRCCS, Endoscopy Unit, Rozzano, Italy (T.R., L.C.)
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan; University of Oslo, Clinical Effectiveness Research Group, and Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway (Y.M.)
| | - Shahnaz Sultan
- Division of Gastroenterology, Hepatology, and Nutrition, University of Minnesota, and VA Health Care System, Minneapolis, Minnesota (S.S.)
| | - Antonio Facciorusso
- University of Foggia, Department of Medical Sciences, Section of Gastroenterology, Foggia, Italy (A.F.)
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli, Ariccia, and Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Rome, Italy (G.A.)
| | - Marco Spadaccini
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, and Humanitas Clinical and Research Center IRCCS, Endoscopy Unit, Rozzano, Italy (C.H., M.S., A.R.)
| | - Britt B S L Houwen
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands (B.B.S.L.H.)
| | | | - Harsh Patel
- Kansas City VA Medical Center, Gastroenterology and Hepatology, Kansas City, Missouri (H.P., P.S.)
| | - Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada (K.K.)
| | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, and Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore (J.W.L.)
| | - Gloria M Fernandez
- Endoscopy Unit, Gastroenterology Department, Clinical Institute of Digestive and Metabolic Disease, Hospital Clínic de Barcelona, University of Barcelona, Barcelona, Spain (G.M.F.)
| | - Pradeep Bhandari
- Queen Alexandra Hospital, Department of Gastroenterology, Portsmouth, United Kingdom (P.B.)
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, and Bergman Clinics Maag and Darm Amsterdam, Amsterdam, the Netherlands (E.D.)
| | - Seth Gross
- Department of Gastroenterology, Tisch Hospital, New York University Langone Medical Center, New York, New York (S.G.)
| | - Tyler Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts (T.B.)
| | - Per Olav Vandvik
- Department of Medicine, Lovisenberg Diaconal Hospital, Oslo, Norway (P.O.V.)
| | - Loredana Correale
- Humanitas Clinical and Research Center IRCCS, Endoscopy Unit, Rozzano, Italy (T.R., L.C.)
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan (M.M., S.K.)
| | - Prateek Sharma
- Kansas City VA Medical Center, Gastroenterology and Hepatology, Kansas City, Missouri (H.P., P.S.)
| | - Douglas K Rex
- Indiana University School of Medicine, Division of Gastroenterology, Indianapolis, Indiana (D.K.R.)
| | - Alessandro Repici
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, and Humanitas Clinical and Research Center IRCCS, Endoscopy Unit, Rozzano, Italy (C.H., M.S., A.R.)
| | - Farid Foroutan
- Ted Rogers Centre for Heart Research, University Health Network, Toronto, Ontario, Canada (F.F.)
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8
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Djinbachian R, Haumesser C, Taghiakbari M, Pohl H, Barkun A, Sidani S, Liu Chen Kiow J, Panzini B, Bouchard S, Deslandres E, Alj A, von Renteln D. Autonomous Artificial Intelligence vs Artificial Intelligence-Assisted Human Optical Diagnosis of Colorectal Polyps: A Randomized Controlled Trial. Gastroenterology 2024; 167:392-399.e2. [PMID: 38331204 DOI: 10.1053/j.gastro.2024.01.044] [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: 11/28/2023] [Revised: 01/15/2024] [Accepted: 01/30/2024] [Indexed: 02/10/2024]
Abstract
BACKGROUND & AIMS Artificial intelligence (AI)-based optical diagnosis systems (CADx) have been developed to allow pathology prediction of colorectal polyps during colonoscopies. However, CADx systems have not yet been validated for autonomous performance. Therefore, we conducted a trial comparing autonomous AI to AI-assisted human (AI-H) optical diagnosis. METHODS We performed a randomized noninferiority trial of patients undergoing elective colonoscopies at 1 academic institution. Patients were randomized into (1) autonomous AI-based CADx optical diagnosis of diminutive polyps without human input or (2) diagnosis by endoscopists who performed optical diagnosis of diminutive polyps after seeing the real-time CADx diagnosis. The primary outcome was accuracy in optical diagnosis in both arms using pathology as the gold standard. Secondary outcomes included agreement with pathology for surveillance intervals. RESULTS A total of 467 patients were randomized (238 patients/158 polyps in the autonomous AI group and 229 patients/179 polyps in the AI-H group). Accuracy for optical diagnosis was 77.2% (95% confidence interval [CI], 69.7-84.7) in the autonomous AI group and 72.1% (95% CI, 65.5-78.6) in the AI-H group (P = .86). For high-confidence diagnoses, accuracy for optical diagnosis was 77.2% (95% CI, 69.7-84.7) in the autonomous AI group and 75.5% (95% CI, 67.9-82.0) in the AI-H group. Autonomous AI had statistically significantly higher agreement with pathology-based surveillance intervals compared to AI-H (91.5% [95% CI, 86.9-96.1] vs 82.1% [95% CI, 76.5-87.7]; P = .016). CONCLUSIONS Autonomous AI-based optical diagnosis exhibits noninferior accuracy to endoscopist-based diagnosis. Both autonomous AI and AI-H exhibited relatively low accuracy for optical diagnosis; however, autonomous AI achieved higher agreement with pathology-based surveillance intervals. (ClinicalTrials.gov, Number NCT05236790).
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Affiliation(s)
- Roupen Djinbachian
- Montreal University Hospital Research Center, Montreal, Quebec, Canada; Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Claire Haumesser
- Montreal University Hospital Research Center, Montreal, Quebec, Canada
| | - Mahsa Taghiakbari
- Montreal University Hospital Research Center, Montreal, Quebec, Canada; Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Heiko Pohl
- Section of Gastroenterology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire; Department of Gastroenterology, Veterans Affairs White River Junction, Vermont
| | - Alan Barkun
- Division of Gastroenterology, McGill University and McGill University Health Center, Montreal, Quebec, Canada
| | - Sacha Sidani
- Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Jeremy Liu Chen Kiow
- Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Benoit Panzini
- Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Simon Bouchard
- Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Erik Deslandres
- Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Abla Alj
- Division of Internal Medicine, Montreal University Hospital Center, Montreal, Quebec, Canada
| | - Daniel von Renteln
- Montreal University Hospital Research Center, Montreal, Quebec, Canada; Division of Gastroenterology, Montreal University Hospital Center, Montreal, Quebec, Canada.
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9
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van der Zander QEW, Roumans R, Kusters CHJ, Dehghani N, Masclee AAM, de With PHN, van der Sommen F, Snijders CCP, Schoon EJ. Appropriate trust in artificial intelligence for the optical diagnosis of colorectal polyps: The role of human/artificial intelligence interaction. Gastrointest Endosc 2024:S0016-5107(24)03324-8. [PMID: 38942330 DOI: 10.1016/j.gie.2024.06.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/26/2024] [Accepted: 06/19/2024] [Indexed: 06/30/2024]
Abstract
BACKGROUND AND AIMS Computer-aided diagnosis (CADx) for the optical diagnosis of colorectal polyps is thoroughly investigated. However, studies on human-artificial intelligence interaction are lacking. Our aim was to investigate endoscopists' trust in CADx by evaluating whether communicating a calibrated algorithm confidence score improved trust. METHODS Endoscopists optically diagnosed 60 colorectal polyps. Initially, endoscopists diagnosed the polyps without CADx assistance (initial diagnosis). Immediately afterward, the same polyp was again shown with a CADx prediction: either only a prediction (benign or premalignant) or a prediction accompanied by a calibrated confidence score (0-100). A confidence score of 0 indicated a benign prediction, 100 a (pre)malignant prediction. In half of the polyps, CADx was mandatory, and for the other half, CADx was optional. After reviewing the CADx prediction, endoscopists made a final diagnosis. Histopathology was used as the gold standard. Endoscopists' trust in CADx was measured as CADx prediction utilization: the willingness to follow CADx predictions when the endoscopists initially disagreed with the CADx prediction. RESULTS Twenty-three endoscopists participated. Presenting CADx predictions increased the endoscopists' diagnostic accuracy (69.3% initial vs 76.6% final diagnosis, P < .001). The CADx prediction was used in 36.5% (n = 183 of 501) disagreements. Adding a confidence score led to lower CADx prediction utilization, except when the confidence score surpassed 60. Mandatory CADx decreased CADx prediction utilization compared to optional CADx. Appropriate trust-using correct or disregarding incorrect CADx predictions-was 48.7% (n = 244 of 501). CONCLUSIONS Appropriate trust was common, and CADx prediction utilization was highest for the optional CADx without confidence scores. These results express the importance of a better understanding of human-artificial intelligence interaction.
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Affiliation(s)
- Quirine E W van der Zander
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, The Netherlands; GROW, School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.
| | - Rachel Roumans
- Human-Technology Interaction, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Carolus H J Kusters
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven The Netherlands
| | - Nikoo Dehghani
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven The Netherlands
| | - Ad A M Masclee
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Peter H N de With
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven The Netherlands
| | - Chris C P Snijders
- Human-Technology Interaction, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Erik J Schoon
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, The Netherlands; Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
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10
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Cheng Y, Li L, Bi Y, Su S, Zhang B, Feng X, Wang N, Zhang W, Yao Y, Ru N, Xiang J, Sun L, Hu K, Wen F, Wang Z, Bai L, Wang X, Wang R, Lv X, Wang P, Meng F, Xiao W, Linghu E, Chai N. Computer-aided diagnosis system for optical diagnosis of colorectal polyps under white light imaging. Dig Liver Dis 2024:S1590-8658(24)00723-0. [PMID: 38744557 DOI: 10.1016/j.dld.2024.04.023] [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] [Received: 12/31/2023] [Revised: 03/21/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
OBJECTIVES This study presents a novel computer-aided diagnosis (CADx) designed for optically diagnosing colorectal polyps using white light imaging (WLI).We aimed to evaluate the effectiveness of the CADx and its auxiliary role among endoscopists with different levels of expertise. METHODS We collected 2,324 neoplastic and 3,735 nonneoplastic polyp WLI images for model training, and 838 colorectal polyp images from 740 patients for model validation. We compared the diagnostic accuracy of the CADx with that of 15 endoscopists under WLI and narrow band imaging (NBI). The auxiliary benefits of CADx for endoscopists of different experience levels and for identifying different types of colorectal polyps was also evaluated. RESULTS The CADx demonstrated an optical diagnostic accuracy of 84.49%, showing considerable superiority over all endoscopists, irrespective of whether WLI or NBI was used (P < 0.001). Assistance from the CADx significantly improved the diagnostic accuracy of the endoscopists from 68.84% to 77.49% (P = 0.001), with the most significant impact observed among novice endoscopists. Notably, novices using CADx-assisted WLI outperform junior and expert endoscopists without such assistance. CONCLUSIONS The CADx demonstrated a crucial role in substantially enhancing the precision of optical diagnosis for colorectal polyps under WLI and showed the greatest auxiliary benefits for novice endoscopists.
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Affiliation(s)
- Yaxuan Cheng
- Chinese PLA Medical School, Beijing, 100853, PR China; Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Longsong Li
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Yawei Bi
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Song Su
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Bo Zhang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xiuxue Feng
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Nanjun Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Wengang Zhang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Yi Yao
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Nan Ru
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Jingyuan Xiang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Lihua Sun
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Kang Hu
- Department of Gastroenterology, The 987 Hospital of PLA Joint Logistic Support Force, Baoji, 721004, PR China
| | - Feng Wen
- Department of Gastroenterology, General Hospital of Central Theater Command of PLA,Wuhan 430070, PR China
| | - Zixin Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Lu Bai
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xueting Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Runzi Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xingping Lv
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Pengju Wang
- Chinese PLA Medical School, Beijing, 100853, PR China; Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Fanqi Meng
- Medical Department, HighWise Medical Technology Co, Ltd, Changsha, 410000, PR China
| | - Wen Xiao
- Medical Department, HighWise Medical Technology Co, Ltd, Changsha, 410000, PR China
| | - Enqiang Linghu
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China.
| | - Ningli Chai
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China.
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11
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Rondonotti E, Bergna IMB, Paggi S, Amato A, Andrealli A, Scardino G, Tamanini G, Lenoci N, Mandelli G, Terreni N, Rocchetto SI, Piagnani A, Di Paolo D, Bina N, Filippi E, Ambrosiani L, Hassan C, Correale L, Radaelli F. White light computer-aided optical diagnosis of diminutive colorectal polyps in routine clinical practice. Endosc Int Open 2024; 12:E676-E683. [PMID: 38774861 PMCID: PMC11108657 DOI: 10.1055/a-2303-0922] [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: 07/05/2023] [Accepted: 04/04/2024] [Indexed: 05/24/2024] Open
Abstract
Background and study aims Artificial Intelligence (AI) systems could make the optical diagnosis (OD) of diminutive colorectal polyps (DCPs) more reliable and objective. This study was aimed at prospectively evaluating feasibility and diagnostic performance of AI-standalone and AI-assisted OD of DCPs in a real-life setting by using a white light-based system (GI Genius, Medtronic Co, Minneapolis, Minnesota, United States). Patients and methods Consecutive colonoscopy outpatients with at least one DCP were evaluated by 11 endoscopists (5 experts and 6 non-experts in OD). DCPs were classified in real time by AI (AI-standalone OD) and by the endoscopist with the assistance of AI (AI-assisted OD), with histopathology as the reference standard. Results Of the 480 DCPs, AI provided the outcome "adenoma" or "non-adenoma" in 81.4% (95% confidence interval [CI]: 77.5-84.6). Sensitivity, specificity, positive and negative predictive value, and accuracy of AI-standalone OD were 97.0% (95% CI 94.0-98.6), 38.1% (95% CI 28.9-48.1), 80.1% (95% CI 75.2-84.2), 83.3% (95% CI 69.2-92.0), and 80.5% (95% CI 68.7-82.8%), respectively. Compared with AI-standalone, the specificity of AI-assisted OD was significantly higher (58.9%, 95% CI 49.7-67.5) and a trend toward an increase was observed for other diagnostic performance measures. Overall accuracy and negative predictive value of AI-assisted OD for experts and non-experts were 85.8% (95% CI 80.0-90.4) vs. 80.1% (95% CI 73.6-85.6) and 89.1% (95% CI 75.6-95.9) vs. 80.0% (95% CI 63.9-90.4), respectively. Conclusions Standalone AI is able to provide an OD of adenoma/non-adenoma in more than 80% of DCPs, with a high sensitivity but low specificity. The human-machine interaction improved diagnostic performance, especially when experts were involved.
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Affiliation(s)
| | - Irene Maria Bambina Bergna
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- University of Milan, Milano, Italy
- Gastroenterology and Digestive Endoscopy Unit, Alessandro Manzoni Hospital, Lecco, Italy
| | - Silvia Paggi
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | - Arnaldo Amato
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Gastroenterology and Digestive Endoscopy Unit, Alessandro Manzoni Hospital, Lecco, Italy
| | | | | | | | | | | | | | - SImone Rocchetto
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- University of Milan, Milano, Italy
| | - Alessandra Piagnani
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- University of Milan, Milano, Italy
| | | | - Niccolò Bina
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | | | | | - Cesare Hassan
- Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Loredana Correale
- Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano, Italy
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12
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Rey JF. As how artificial intelligence is revolutionizing endoscopy. Clin Endosc 2024; 57:302-308. [PMID: 38454543 PMCID: PMC11133999 DOI: 10.5946/ce.2023.230] [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] [Received: 09/13/2023] [Revised: 10/11/2023] [Accepted: 10/15/2023] [Indexed: 03/09/2024] Open
Abstract
With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.
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Affiliation(s)
- Jean-Francois Rey
- Institut Arnaut Tzanck Gastrointestinal Unt, Saint Laurent du Var, France
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13
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Kobayashi R, Yoshida N, Tomita Y, Hashimoto H, Inoue K, Hirose R, Dohi O, Inada Y, Murakami T, Morimoto Y, Zhu X, Itoh Y. Detailed Superiority of the CAD EYE Artificial Intelligence System over Endoscopists for Lesion Detection and Characterization Using Unique Movie Sets. J Anus Rectum Colon 2024; 8:61-69. [PMID: 38689788 PMCID: PMC11056537 DOI: 10.23922/jarc.2023-041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/22/2023] [Indexed: 05/02/2024] Open
Abstract
Objectives Detailed superiority of CAD EYE (Fujifilm, Tokyo, Japan), an artificial intelligence for polyp detection/diagnosis, compared to endoscopists is not well examined. We examined endoscopist's ability using movie sets of colorectal lesions which were detected and diagnosed by CAD EYE accurately. Methods Consecutive lesions of ≤10 mm were examined live by CAD EYE from March-June 2022 in our institution. Short unique movie sets of each lesion with and without CAD EYE were recorded simultaneously using two recorders for detection under white light imaging (WLI) and linked color imaging (LCI) and diagnosis under blue laser/light imaging (BLI). Excluding inappropriate movies, 100 lesions detected and diagnosed with CAD EYE accurately were evaluated. Movies without CAD EYE were evaluated first by three trainees and three experts. Subsequently, movies with CAD EYE were examined. The rates of accurate detection and diagnosis were evaluated for both movie sets. Results Among 100 lesions (mean size: 4.7±2.6 mm; 67 neoplastic/33 hyperplastic), mean accurate detection rates of movies without or with CAD EYE were 78.7%/96.7% under WLI (p<0.01) and 91.3%/97.3% under LCI (p<0.01) for trainees and 85.3%/99.0% under WLI (p<0.01) and 92.6%/99.3% under LCI (p<0.01) for experts. Mean accurate diagnosis rates of movies without or with CAD EYE for BLI were 85.3%/100% for trainees (p<0.01) and 92.3%/100% for experts (p<0.01), respectively. The significant risk factors of not-detected lesions for trainees were right-sided, hyperplastic, not-reddish, in the corner, halation, and inadequate bowel preparation. Conclusions Unique movie sets with and without CAD EYE could suggest it's efficacy for lesion detection/diagnosis.
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Affiliation(s)
- Reo Kobayashi
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Naohisa Yoshida
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yuri Tomita
- Department of Gastroenterology, Kosekai Takeda Hosptal, Kyoto, Japan
| | - Hikaru Hashimoto
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Ken Inoue
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Ryohei Hirose
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Osamu Dohi
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yutaka Inada
- Department of Gastroenterology, Kyoto First Red Cross Hospital, Kyoto, Japan
| | - Takaaki Murakami
- Department of Gastroenterology, Aiseikai Yamashina Hospital, Kyoto, Japan
| | - Yasutaka Morimoto
- Department of Gastroenterology, Kyoto Saiseikai Hospital, Kyoto, Japan
| | - Xin Zhu
- Graduate School of Computer Science and Engineering, The University of Aizu, Fukushima, Japan
| | - Yoshito Itoh
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
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Groza AL, Miutescu B, Tefas C, Popa A, Ratiu I, Sirli R, Popescu A, Motofelea AC, Tantau M. Evaluating the Efficacy of Resect-and-Discard and Resect-and-Retrieve Strategies for Diminutive Colonic Polyps. Life (Basel) 2024; 14:532. [PMID: 38672802 PMCID: PMC11051488 DOI: 10.3390/life14040532] [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: 03/24/2024] [Revised: 04/11/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Diminutive polyps present a unique challenge in colorectal cancer (CRC) prevention strategies. This study aims to assess the characteristics and variables of diminutive polyps in a Romanian cohort, intending to develop a combined resect-and-retrieve or resect-and-discard strategy that reduces the need for an optical diagnosis. MATERIALS AND METHODS A prospective cohort study was conducted at two endoscopy centers in Romania from July to December 2021. Adult patients undergoing colonoscopies where polyps were identified and resected were included. Endoscopic procedures employed advanced diagnostic features, including blue-light imaging (BLI) and narrow-band imaging (NBI). Logistic regression analysis was utilized to determine factors impacting the probability of adenomatous polyps with high-grade dysplasia (HGD). RESULTS A total of 427 patients were included, with a mean age of 59.42 years (±11.19), predominantly male (60.2%). The most common indication for a colonoscopy was lower gastrointestinal symptoms (42.6%), followed by screening (28.8%). Adequate bowel preparation was achieved in 87.8% of cases. The logistic regression analysis revealed significant predictors of HGD in adenomatous polyps: age (OR = 1.05, 95% CI: 1.01-1.08, p = 0.01) and polyp size (>5 mm vs. ≤5 mm, OR = 4.4, 95% CI: 1.94-10.06, p < 0.001). Polyps classified as Paris IIa, Ip, and Isp were significantly more likely to harbor HGD compared to the reference group (Is), with odds ratios of 6.05, 3.68, and 2.7, respectively. CONCLUSIONS The study elucidates significant associations between the presence of HGD in adenomatous polyps and factors such as age, polyp size, and Paris classification. These findings support the feasibility of a tailored approach in the resect-and-discard and resect-and-retrieve strategies for diminutive polyps, potentially optimizing CRC prevention and intervention practices. Further research is warranted to validate these strategies in broader clinical settings.
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Affiliation(s)
- Andrei Lucian Groza
- 3rd Department of Internal Medicine, Iuliu Haţieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (A.L.G.); (C.T.); (M.T.)
| | - Bogdan Miutescu
- Advanced Regional Research Center in Gastroenterology and Hepatology, Department VII: Internal Medicine II, Discipline of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania; (A.P.); (I.R.); (R.S.); (A.P.)
| | - Cristian Tefas
- 3rd Department of Internal Medicine, Iuliu Haţieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (A.L.G.); (C.T.); (M.T.)
- Regional Institute of Gastroenterology and Hepatology “Prof. Dr. Octavian Fodor”, 400162 Cluj-Napoca, Romania
| | - Alexandru Popa
- Advanced Regional Research Center in Gastroenterology and Hepatology, Department VII: Internal Medicine II, Discipline of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania; (A.P.); (I.R.); (R.S.); (A.P.)
| | - Iulia Ratiu
- Advanced Regional Research Center in Gastroenterology and Hepatology, Department VII: Internal Medicine II, Discipline of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania; (A.P.); (I.R.); (R.S.); (A.P.)
| | - Roxana Sirli
- Advanced Regional Research Center in Gastroenterology and Hepatology, Department VII: Internal Medicine II, Discipline of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania; (A.P.); (I.R.); (R.S.); (A.P.)
| | - Alina Popescu
- Advanced Regional Research Center in Gastroenterology and Hepatology, Department VII: Internal Medicine II, Discipline of Gastroenterology and Hepatology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania; (A.P.); (I.R.); (R.S.); (A.P.)
| | - Alexandru Catalin Motofelea
- Department of Internal Medicine, Faculty of Medicine, “Victor Babeș” University of Medicine and Pharmacy, 300041 Timisoara, Romania;
| | - Marcel Tantau
- 3rd Department of Internal Medicine, Iuliu Haţieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (A.L.G.); (C.T.); (M.T.)
- Regional Institute of Gastroenterology and Hepatology “Prof. Dr. Octavian Fodor”, 400162 Cluj-Napoca, Romania
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15
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Jukema JB, Kusters CHJ, Jong MR, Fockens KN, Boers T, van der Putten JA, Pouw RE, Duits LC, Weusten BLAM, Herrero LA, Houben MHMG, Nagengast WB, Westerhof J, Alkhalaf A, Mallant-Hent R, Scholten P, Ragunath K, Seewald S, Elbe P, Silva FB, Barret M, Ortiz Fernández-Sordo J, Moral Villarejo G, Pech O, Beyna T, Montazeri NSM, der Sommen FV, de With PH, de Groof AJ, Bergman JJ. Computer-aided diagnosis improves characterization of Barrett's neoplasia by general endoscopists (with video). Gastrointest Endosc 2024:S0016-5107(24)00233-5. [PMID: 38636819 DOI: 10.1016/j.gie.2024.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/25/2024] [Accepted: 04/08/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND AND AIMS Characterization of visible abnormalities in patients with Barrett's esophagus (BE) can be challenging, especially for inexperienced endoscopists. This results in suboptimal diagnostic accuracy and poor interobserver agreement. Computer-aided diagnosis (CADx) systems may assist endoscopists. We aimed to develop, validate, and benchmark a CADx system for BE neoplasia. METHODS The CADx system received pretraining with ImageNet and then consecutive domain-specific pretraining with GastroNet, which includes 5 million endoscopic images. It was subsequently trained and internally validated using 1758 narrow-band imaging (NBI) images of early BE neoplasia (352 patients) and 1838 NBI images of nondysplastic BE (173 patients) from 8 international centers. CADx was tested prospectively on corresponding image and video test sets with 30 cases (20 patients) of BE neoplasia and 60 cases (31 patients) of nondysplastic BE. The test set was benchmarked by 44 general endoscopists in 2 phases (phase 1, no CADx assistance; phase 2, with CADx assistance). Ten international BE experts provided additional benchmark performance. RESULTS Stand-alone sensitivity and specificity of the CADx system were 100% and 98% for images and 93% and 96% for videos, respectively. CADx outperformed general endoscopists without CADx assistance in terms of sensitivity (P = .04). Sensitivity and specificity of general endoscopists increased from 84% to 96% and 90% to 98% with CAD assistance (P < .001). CADx assistance increased endoscopists' confidence in characterization (P < .001). CADx performance was similar to that of the BE experts. CONCLUSIONS CADx assistance significantly increased characterization performance of BE neoplasia by general endoscopists to the level of expert endoscopists. The use of this CADx system may thereby improve daily Barrett surveillance.
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Affiliation(s)
- Jelmer B Jukema
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Carolus H J Kusters
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Martijn R Jong
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Kiki N Fockens
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Tim Boers
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Joost A van der Putten
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Roos E Pouw
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Lucas C Duits
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Bas L A M Weusten
- Department of Gastroenterology and Hepatology, UMC Utrecht, University of Utrecht, Utrecht, the Netherlands; Department of Gastroenterology and Hepatology, Sint Antonius Hospital, Nieuwegein, the Netherlands
| | - Lorenza Alvarez Herrero
- Department of Gastroenterology and Hepatology, Sint Antonius Hospital, Nieuwegein, the Netherlands
| | - Martin H M G Houben
- Department of Gastroenterology and Hepatology, HagaZiekenhuis Den Haag, Den Haag, the Netherlands
| | - Wouter B Nagengast
- Department of Gastroenterology and Hepatology, UMC Groningen, University of Groningen, Groningen, the Netherlands
| | - Jessie Westerhof
- Department of Gastroenterology and Hepatology, UMC Groningen, University of Groningen, Groningen, the Netherlands
| | - Alaa Alkhalaf
- Department of Gastroenterology and Hepatology, Isala Hospital Zwolle, Zwolle, the Netherlands
| | - Rosalie Mallant-Hent
- Department of Gastroenterology and Hepatology, Flevoziekenhuis Almere, Almere, the Netherlands
| | - Pieter Scholten
- Department of Gastroenterology and Hepatology, Onze Lieve Vrouwe Gasthuis, Amsterdam, the Netherlands
| | - Krish Ragunath
- Department of Gastroenterology and Hepatology, Royal Perth Hospital, Curtin University, Perth, WA, Australia
| | - Stefan Seewald
- Department of Gastroenterology and Hepatology, Hirslanden Klinik, Zurich, Switzerland
| | - Peter Elbe
- Department of Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden; Division of Surgery, Department of Clinical Science, Intervention and Technology, CLINTEC, Karolinska Institutet, Stockholm, Sweden
| | - Francisco Baldaque Silva
- Department of Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden; Center for Advanced Endoscopy Carlos Moreira da Silva, Gastroenterology Department, Pedro Hispano Hospital, ULSM Matosinhos, Portugal
| | - Maximilien Barret
- Department of Gastroenterology and Hepatology, Cochin Hospital Paris, Paris, France
| | - Jacobo Ortiz Fernández-Sordo
- Department of Gastroenterology and Hepatology, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
| | - Guiomar Moral Villarejo
- Department of Gastroenterology and Hepatology, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
| | - Oliver Pech
- Department of Gastroenterology and Hepatology, St. John of God Hospital, Regensburg, Germany
| | - Torsten Beyna
- Department of Gastroenterology and Hepatology, Evangalisches Krankenhaus Düsseldorf, Düsseldorf, Germany
| | - Nahid S M Montazeri
- Biostatistics Unit, Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Peter H de With
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - A Jeroen de Groof
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Jacques J Bergman
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
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16
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Lopes SR, Martins C, Santos IC, Teixeira M, Gamito É, Alves AL. Colorectal cancer screening: A review of current knowledge and progress in research. World J Gastrointest Oncol 2024; 16:1119-1133. [PMID: 38660635 PMCID: PMC11037045 DOI: 10.4251/wjgo.v16.i4.1119] [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: 12/28/2023] [Revised: 01/16/2024] [Accepted: 02/18/2024] [Indexed: 04/10/2024] Open
Abstract
Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, being the third most commonly diagnosed malignancy and the second leading cause of cancer-related deaths globally. Despite the progress in screening, early diagnosis, and treatment, approximately 20%-25% of CRC patients still present with metastatic disease at the time of their initial diagnosis. Furthermore, the burden of disease is still expected to increase, especially in individuals younger than 50 years old, among whom early-onset CRC incidence has been increasing. Screening and early detection are pivotal to improve CRC-related outcomes. It is well established that CRC screening not only reduces incidence, but also decreases deaths from CRC. Diverse screening strategies have proven effective in decreasing both CRC incidence and mortality, though variations in efficacy have been reported across the literature. However, uncertainties persist regarding the optimal screening method, age intervals and periodicity. Moreover, adherence to CRC screening remains globally low. In recent years, emerging technologies, notably artificial intelligence, and non-invasive biomarkers, have been developed to overcome these barriers. However, controversy exists over the actual impact of some of the new discoveries on CRC-related outcomes and how to effectively integrate them into daily practice. In this review, we aim to cover the current evidence surrounding CRC screening. We will further critically assess novel approaches under investigation, in an effort to differentiate promising innovations from mere novelties.
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Affiliation(s)
- Sara Ramos Lopes
- Department of Gastroenterology, Centro Hospitalar de Setúbal, Setúbal 2910-446, Portugal
| | - Claudio Martins
- Department of Gastroenterology, Centro Hospitalar de Setúbal, Setúbal 2910-446, Portugal
| | - Inês Costa Santos
- Department of Gastroenterology, Centro Hospitalar de Setúbal, Setúbal 2910-446, Portugal
| | - Madalena Teixeira
- Department of Gastroenterology, Centro Hospitalar de Setúbal, Setúbal 2910-446, Portugal
| | - Élia Gamito
- Department of Gastroenterology, Centro Hospitalar de Setúbal, Setúbal 2910-446, Portugal
| | - Ana Luisa Alves
- Department of Gastroenterology, Centro Hospitalar de Setúbal, Setúbal 2910-446, Portugal
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17
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Rondonotti E, Radaelli F. Machines Advancing: Marginalization of Human Beings on the Horizon? Gastroenterology 2024:S0016-5085(24)00295-6. [PMID: 38499163 DOI: 10.1053/j.gastro.2024.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Accepted: 03/12/2024] [Indexed: 03/20/2024]
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18
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van der Zander QEW, Schreuder RM, Thijssen A, Kusters CHJ, Dehghani N, Scheeve T, Winkens B, van der Ende - van Loon MCM, de With PHN, van der Sommen F, Masclee AAM, Schoon EJ. Artificial intelligence for characterization of diminutive colorectal polyps: A feasibility study comparing two computer-aided diagnosis systems. Artif Intell Gastrointest Endosc 2024; 5:90574. [DOI: 10.37126/aige.v5.i1.90574] [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: 12/07/2023] [Revised: 01/11/2024] [Accepted: 02/02/2024] [Indexed: 02/20/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has potential in the optical diagnosis of colorectal polyps.
AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system (CADx) AI for ColoRectal Polyps (AI4CRP) for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYETM (Fujifilm, Tokyo, Japan). CADx influence on the optical diagnosis of an expert endoscopist was also investigated.
METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm. Both CADx-systems exploit convolutional neural networks. Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard. AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value (range 0.0-1.0). A predefined cut-off value of 0.6 was set with values < 0.6 indicating benign and values ≥ 0.6 indicating premalignant colorectal polyps. Low confidence characterizations were defined as values 40% around the cut-off value of 0.6 (< 0.36 and > 0.76). Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.
RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps. Self-critical AI4CRP, excluding 14 low confidence characterizations [27.5% (14/51)], had a diagnostic accuracy of 89.2%, sensitivity of 89.7%, and specificity of 87.5%, which was higher compared to AI4CRP. CAD EYE had a 83.7% diagnostic accuracy, 74.2% sensitivity, and 100.0% specificity. Diagnostic performances of the endoscopist alone (before AI) increased non-significantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE (AI-assisted endoscopist). Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems, except for specificity for which CAD EYE performed best.
CONCLUSION Real-time use of AI4CRP was feasible. Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.
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Affiliation(s)
- Quirine Eunice Wennie van der Zander
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
| | - Ramon M Schreuder
- Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven 5602 ZA, Netherlands
| | - Ayla Thijssen
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
| | - Carolus H J Kusters
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Nikoo Dehghani
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Thom Scheeve
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Bjorn Winkens
- Department of Methodology and Statistics, Maastricht University, Postbus 616, 6200 MD Maastricht, Netherlands
- School for Public Health and Primary Care, Maastricht University, Maastricht 6200 MD, Netherlands
| | | | - Peter H N de With
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Ad A M Masclee
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
| | - Erik J Schoon
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
- Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven 5602 ZA, Netherlands
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19
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Kato S, Kudo SE, Minegishi Y, Miyata Y, Maeda Y, Kuroki T, Takashina Y, Mochizuki K, Tamura E, Abe M, Sato Y, Sakurai T, Kouyama Y, Tanaka K, Ogawa Y, Nakamura H, Ichimasa K, Ogata N, Hisayuki T, Hayashi T, Wakamura K, Miyachi H, Baba T, Ishida F, Nemoto T, Misawa M. Impact of computer-aided characterization for diagnosis of colorectal lesions, including sessile serrated lesions: Multireader, multicase study. Dig Endosc 2024; 36:341-350. [PMID: 37937532 DOI: 10.1111/den.14612] [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: 02/23/2023] [Accepted: 06/06/2023] [Indexed: 11/09/2023]
Abstract
OBJECTIVES Computer-aided characterization (CADx) may be used to implement optical biopsy strategies into colonoscopy practice; however, its impact on endoscopic diagnosis remains unknown. We aimed to evaluate the additional diagnostic value of CADx when used by endoscopists for assessing colorectal polyps. METHODS This was a single-center, multicase, multireader, image-reading study using randomly extracted images of pathologically confirmed polyps resected between July 2021 and January 2022. Approved CADx that could predict two-tier classification (neoplastic or nonneoplastic) by analyzing narrow-band images of the polyps was used to obtain a CADx diagnosis. Participating endoscopists determined if the polyps were neoplastic or not and noted their confidence level using a computer-based, image-reading test. The test was conducted twice with a 4-week interval: the first test was conducted without CADx prediction and the second test with CADx prediction. Diagnostic performances for neoplasms were calculated using the pathological diagnosis as reference and performances with and without CADx prediction were compared. RESULTS Five hundred polyps were randomly extracted from 385 patients and diagnosed by 14 endoscopists (including seven experts). The sensitivity for neoplasia was significantly improved by referring to CADx (89.4% vs. 95.6%). CADx also had incremental effects on the negative predictive value (69.3% vs. 84.3%), overall accuracy (87.2% vs. 91.8%), and high-confidence diagnosis rate (77.4% vs. 85.8%). However, there was no significant difference in specificity (80.1% vs. 78.9%). CONCLUSIONS Computer-aided characterization has added diagnostic value for differentiating colorectal neoplasms and may improve the high-confidence diagnosis rate.
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Affiliation(s)
- Shun Kato
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yosuke Minegishi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuki Miyata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Takanori Kuroki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuki Takashina
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kenichi Mochizuki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Eri Tamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Masahiro Abe
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuta Sato
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Tatsuya Sakurai
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuta Kouyama
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kenta Tanaka
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yushi Ogawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hiroki Nakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
- Department of Gastroenterology and Hepatology, National University Hospital, Singapore City, Singapore
| | - Noriyuki Ogata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Tomokazu Hisayuki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Takemasa Hayashi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kunihiko Wakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hideyuki Miyachi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Toshiyuki Baba
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Fumio Ishida
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Tetsuo Nemoto
- Department of Diagnostic Pathology and Laboratory Medicine, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
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20
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Antonelli G, Voiosu AM, Pawlak KM, Gonçalves TC, Le N, Bronswijk M, Hollenbach M, Elshaarawy O, Beilenhoff U, Mascagni P, Voiosu T, Pellisé M, Dinis-Ribeiro M, Triantafyllou K, Arvanitakis M, Bisschops R, Hassan C, Messmann H, Gralnek IM. Training in basic gastrointestinal endoscopic procedures: a European Society of Gastrointestinal Endoscopy (ESGE) and European Society of Gastroenterology and Endoscopy Nurses and Associates (ESGENA) Position Statement. Endoscopy 2024; 56:131-150. [PMID: 38040025 DOI: 10.1055/a-2205-2613] [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: 12/03/2023]
Abstract
This ESGE Position Statement provides structured and evidence-based guidance on the essential requirements and processes involved in training in basic gastrointestinal (GI) endoscopic procedures. The document outlines definitions; competencies required, and means to their assessment and maintenance; the structure and requirements of training programs; patient safety and medicolegal issues. 1: ESGE and ESGENA define basic endoscopic procedures as those procedures that are commonly indicated, generally accessible, and expected to be mastered (technically and cognitively) by the end of any core training program in gastrointestinal endoscopy. 2: ESGE and ESGENA consider the following as basic endoscopic procedures: diagnostic upper and lower GI endoscopy, as well as a limited range of interventions such as: tissue acquisition via cold biopsy forceps, polypectomy for lesions ≤ 10 mm, hemostasis techniques, enteral feeding tube placement, foreign body retrieval, dilation of simple esophageal strictures, and India ink tattooing of lesion location. 3: ESGE and ESGENA recommend that training in GI endoscopy should be subject to stringent formal requirements that ensure all ESGE key performance indicators (KPIs) are met. 4: Training in basic endoscopic procedures is a complex process and includes the development and acquisition of cognitive, technical/motor, and integrative skills. Therefore, ESGE and ESGENA recommend the use of validated tools to track the development of skills and assess competence. 5: ESGE and ESGENA recommend incorporating a multimodal approach to evaluating competence in basic GI endoscopic procedures, including procedural thresholds and the measurement and documentation of established ESGE KPIs. 7: ESGE and ESGENA recommend the continuous monitoring of ESGE KPIs during GI endoscopy training to ensure the trainee's maintenance of competence. 9: ESGE and ESGENA recommend that GI endoscopy training units fulfil the ESGE KPIs for endoscopy units and, furthermore, be capable of providing the dedicated personnel, infrastructure, and sufficient case volume required for successful training within a structured training program. 10: ESGE and ESGENA recommend that trainers in basic GI endoscopic procedures should be endoscopists with formal educational training in the teaching of endoscopy, which allows them to successfully and safely teach trainees.
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Affiliation(s)
- Giulio Antonelli
- Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, "Sapienza" University of Rome, Italy
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli Hospital, Ariccia, Rome, Italy
| | - Andrei M Voiosu
- Department of Gastroenterology, Colentina Clinical Hospital, Bucharest, Romania
- "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Katarzyna M Pawlak
- Endoscopy Unit, Gastroenterology Department, Hospital of the Ministry of Interior and Administration, Szczecin, Poland
- The Center for Therapeutic Endoscopy and Endoscopic Oncology, St. Michael's Hospital, University of Toronto, Ontario, Canada
| | - Tiago Cúrdia Gonçalves
- Gastroenterology Department, Hospital da Senhora da Oliveira, Guimarães, Portugal
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
| | - Nha Le
- Gastroenterology Division, Internal Medicine and Hematology Department, Semmelweis University, Budapest, Hungary
| | - Michiel Bronswijk
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, KU Leuven, Belgium
- Department of Gastroenterology and Hepatology, Imelda General Hospital, Bonheiden, Belgium
| | - Marcus Hollenbach
- Division of Gastroenterology, Medical Department II, University of Leipzig Medical Center, Leipzig, Germany
| | - Omar Elshaarawy
- Hepatology and Gastroenterology Department, National Liver Institute, Menoufia University, Menoufia, Egypt
| | | | - Pietro Mascagni
- IHU Strasbourg, Strasbourg, France
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Theodor Voiosu
- Department of Gastroenterology, Colentina Clinical Hospital, Bucharest, Romania
- "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Maria Pellisé
- Department of Gastroenterology, Hospital Clínic Barcelona, Barcelona, Spain
| | - Mário Dinis-Ribeiro
- Gastroenterology Department, Portuguese Oncology Institute of Porto, Porto, Portugal
- MEDCIDS/Faculty of Medicine, University of Porto, Porto, Portugal
| | | | - Marianna Arvanitakis
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, TARGID, Leuven, Belgium
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, TARGID, Leuven, Belgium
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Endoscopy Unit, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Helmut Messmann
- Department of Gastroenterology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Ian M Gralnek
- Institute of Gastroenterology and Hepatology, Emek Medical Center, Afula, Israel
- Rappaport Faculty of Medicine Technion Israel Institute of Technology, Haifa, Israel
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21
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Halvorsen N, Mori Y. Computer-aided polyp characterization in colonoscopy: sufficient performance or not? Clin Endosc 2024; 57:18-23. [PMID: 38178329 PMCID: PMC10834281 DOI: 10.5946/ce.2023.092] [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] [Received: 03/23/2023] [Revised: 05/03/2023] [Accepted: 05/24/2023] [Indexed: 01/06/2024] Open
Abstract
Computer-assisted polyp characterization (computer-aided diagnosis, CADx) facilitates optical diagnosis during colonoscopy. Several studies have demonstrated high sensitivity and specificity of CADx tools in identifying neoplastic changes in colorectal polyps. To implement CADx tools in colonoscopy, there is a need to confirm whether these tools satisfy the threshold levels that are required to introduce optical diagnosis strategies such as "diagnose-and-leave," "resect-and-discard" or "DISCARD-lite." In this article, we review the available data from prospective trials regarding the effect of multiple CADx tools and discuss whether they meet these thresholds.
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Affiliation(s)
- Natalie Halvorsen
- Clinical Effectiveness Research Group, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Yuichi Mori
- Clinical Effectiveness Research Group, Oslo University Hospital and University of Oslo, Oslo, Norway
- Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
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22
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Yamaguchi D, Shimoda R, Miyahara K, Yukimoto T, Sakata Y, Takamori A, Mizuta Y, Fujimura Y, Inoue S, Tomonaga M, Ogino Y, Eguchi K, Ikeda K, Tanaka Y, Takedomi H, Hidaka H, Akutagawa T, Tsuruoka N, Noda T, Tsunada S, Esaki M. Impact of an artificial intelligence-aided endoscopic diagnosis system on improving endoscopy quality for trainees in colonoscopy: Prospective, randomized, multicenter study. Dig Endosc 2024; 36:40-48. [PMID: 37079002 DOI: 10.1111/den.14573] [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: 01/30/2023] [Accepted: 04/19/2023] [Indexed: 04/21/2023]
Abstract
OBJECTIVE This study was performed to evaluate whether the use of CAD EYE (Fujifilm, Tokyo, Japan) for colonoscopy improves colonoscopy quality in gastroenterology trainees. METHODS The patients in this multicenter randomized controlled trial were divided into Group A (observation using CAD EYE) and Group B (standard observation). Six trainees performed colonoscopies using a back-to-back method in pairs with gastroenterology experts. The primary end-point was the trainees' adenoma detection rate (ADR), and the secondary end-points were the trainees' adenoma miss rate (AMR) and Assessment of Competency in Endoscopy (ACE) tool scores. Each trainee's learning curve was evaluated using a cumulative sum (CUSUM) control chart. RESULTS We analyzed data for 231 patients (Group A, n = 113; Group B, n = 118). The ADR was not significantly different between the two groups. Group A had a significantly lower AMR (25.6% vs. 38.6%, P = 0.033) and number of missed adenomas per patient (0.5 vs. 0.9, P = 0.004) than Group B. Group A also had significantly higher ACE tool scores for pathology identification (2.26 vs. 2.07, P = 0.030) and interpretation and identification of pathology location (2.18 vs. 2.00, P = 0.038). For the CUSUM learning curve, Group A showed a trend toward a lower number of cases of missed multiple adenomas by the six trainees. CONCLUSION CAD EYE did not improve ADR but decreased the AMR and improved the ability to accurately locate and identify colorectal adenomas. CAD EYE can be assumed to be beneficial for improving colonoscopy quality in gastroenterology trainees. TRIAL REGISTRATION University Hospital Medical Information Network Clinical Trials Registry (UMIN000044031).
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Affiliation(s)
- Daisuke Yamaguchi
- Department of Gastroenterology, National Hospital Organization Ureshino Medical Center, Ureshino, Japan
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Ryo Shimoda
- Department of Endoscopic Diagnostics and Therapeutics, Saga University Hospital, Saga, Japan
| | - Koichi Miyahara
- Department of Internal Medicine, Karatsu Red Cross Hospital, Saga, Japan
| | - Takahiro Yukimoto
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Yasuhisa Sakata
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Ayako Takamori
- Clinical Research Center, Saga University Hospital, Saga, Japan
| | - Yumi Mizuta
- Department of Gastroenterology, National Hospital Organization Ureshino Medical Center, Ureshino, Japan
| | - Yutaro Fujimura
- Department of Internal Medicine, Karatsu Red Cross Hospital, Saga, Japan
| | - Suma Inoue
- Department of Internal Medicine, Karatsu Red Cross Hospital, Saga, Japan
| | - Michito Tomonaga
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Yuya Ogino
- Department of Internal Medicine, Karatsu Red Cross Hospital, Saga, Japan
| | - Kohei Eguchi
- Department of Internal Medicine, Karatsu Red Cross Hospital, Saga, Japan
| | - Kei Ikeda
- Department of Gastroenterology, National Hospital Organization Ureshino Medical Center, Ureshino, Japan
| | - Yuichiro Tanaka
- Department of Gastroenterology, National Hospital Organization Ureshino Medical Center, Ureshino, Japan
| | - Hironobu Takedomi
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Hidenori Hidaka
- Department of Internal Medicine, Karatsu Red Cross Hospital, Saga, Japan
| | - Takashi Akutagawa
- Department of Endoscopic Diagnostics and Therapeutics, Saga University Hospital, Saga, Japan
| | - Nanae Tsuruoka
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Takahiro Noda
- Department of Internal Medicine, Karatsu Red Cross Hospital, Saga, Japan
| | - Seiji Tsunada
- Department of Gastroenterology, National Hospital Organization Ureshino Medical Center, Ureshino, Japan
| | - Motohiro Esaki
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
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23
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Ueda T, Li JW, Ho SH, Singh R, Uedo N. Precision endoscopy in the era of climate change and sustainability. J Gastroenterol Hepatol 2024; 39:18-27. [PMID: 37881033 DOI: 10.1111/jgh.16383] [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: 09/08/2023] [Revised: 10/05/2023] [Accepted: 10/07/2023] [Indexed: 10/27/2023]
Abstract
Global warming caused by increased greenhouse gas (GHG) emissions has a direct impact on human health. Gastrointestinal (GI) endoscopy contributes significantly to GHG emissions due to energy consumption, reprocessing of endoscopes and accessories, production of equipment, safe disposal of biohazardous waste, and travel by patients. Moreover, GHGs are also generated in histopathology through tissue processing and the production of biopsy specimen bottles. The reduction in unnecessary surveillance endoscopies and biopsies is a practical approach to decrease GHG emissions without affecting disease outcomes. This narrative review explores the role of precision medicine in GI endoscopy, such as image-enhanced endoscopy and artificial intelligence, with a focus on decreasing unnecessary endoscopic procedures and biopsies in the surveillance and diagnosis of premalignant lesions in the esophagus, stomach, and colon. This review offers strategies to minimize unnecessary endoscopic procedures and biopsies, decrease GHG emissions, and maintain high-quality patient care, thereby contributing to sustainable healthcare practices.
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Affiliation(s)
- Tomoya Ueda
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - James Weiquan Li
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore, Singapore
| | - Shiaw-Hooi Ho
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Rajvinder Singh
- Department of Gastroenterology, Lyell McEwin and Modbury Hospitals, University of Adelaide, Adelaide, Australia
| | - Noriya Uedo
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
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24
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Leśniewska M, Patryn R, Kopystecka A, Kozioł I, Budzyńska J. Third Eye? The Assistance of Artificial Intelligence (AI) in the Endoscopy of Gastrointestinal Neoplasms. J Clin Med 2023; 12:6721. [PMID: 37959187 PMCID: PMC10650785 DOI: 10.3390/jcm12216721] [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: 09/04/2023] [Revised: 10/19/2023] [Accepted: 10/21/2023] [Indexed: 11/15/2023] Open
Abstract
Gastrointestinal cancers are characterized by high incidence and mortality. However, there are well-established methods of screening. The endoscopy exam provides the macroscopical image and enables harvesting the tissue samples for further histopathological diagnosis. The efficiency of endoscopies relies not only on proper patient preparation, but also on the skills of the personnel conducting the exam. In recent years, a number of reports concerning the application of artificial intelligence (AI) in medicine have arisen. Numerous studies aimed to assess the utility of deep learning/ neural network systems supporting endoscopies. In this review, we summarized the most recent reports and randomized clinical trials regarding the application of AI in screening and surveillance of gastrointestinal cancers among patients suffering from esophageal, gastric, and colorectal cancer, along with the advantages, limitations, and controversies of those novel solutions.
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Affiliation(s)
- Magdalena Leśniewska
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Rafał Patryn
- Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland
| | - Agnieszka Kopystecka
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Ilona Kozioł
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Julia Budzyńska
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
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25
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Young E, Edwards L, Singh R. The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization. Cancers (Basel) 2023; 15:5126. [PMID: 37958301 PMCID: PMC10647850 DOI: 10.3390/cancers15215126] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/14/2023] [Accepted: 10/14/2023] [Indexed: 11/15/2023] Open
Abstract
Colorectal cancer remains a leading cause of cancer-related morbidity and mortality worldwide, despite the widespread uptake of population surveillance strategies. This is in part due to the persistent development of 'interval colorectal cancers', where patients develop colorectal cancer despite appropriate surveillance intervals, implying pre-malignant polyps were not resected at a prior colonoscopy. Multiple techniques have been developed to improve the sensitivity and accuracy of lesion detection and characterisation in an effort to improve the efficacy of colorectal cancer screening, thereby reducing the incidence of interval colorectal cancers. This article presents a comprehensive review of the transformative role of artificial intelligence (AI), which has recently emerged as one such solution for improving the quality of screening and surveillance colonoscopy. Firstly, AI-driven algorithms demonstrate remarkable potential in addressing the challenge of overlooked polyps, particularly polyp subtypes infamous for escaping human detection because of their inconspicuous appearance. Secondly, AI empowers gastroenterologists without exhaustive training in advanced mucosal imaging to characterise polyps with accuracy similar to that of expert interventionalists, reducing the dependence on pathologic evaluation and guiding appropriate resection techniques or referrals for more complex resections. AI in colonoscopy holds the potential to advance the detection and characterisation of polyps, addressing current limitations and improving patient outcomes. The integration of AI technologies into routine colonoscopy represents a promising step towards more effective colorectal cancer screening and prevention.
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Affiliation(s)
- Edward Young
- Faculty of Health and Medical Sciences, University of Adelaide, Lyell McEwin Hospital, Haydown Rd, Elizabeth Vale, SA 5112, Australia
| | - Louisa Edwards
- Faculty of Health and Medical Sciences, University of Adelaide, Queen Elizabeth Hospital, Port Rd, Woodville South, SA 5011, Australia
| | - Rajvinder Singh
- Faculty of Health and Medical Sciences, University of Adelaide, Lyell McEwin Hospital, Haydown Rd, Elizabeth Vale, SA 5112, Australia
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26
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Vadhwana B, Tarazi M, Patel V. The Role of Artificial Intelligence in Prospective Real-Time Histological Prediction of Colorectal Lesions during Colonoscopy: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2023; 13:3267. [PMID: 37892088 PMCID: PMC10606449 DOI: 10.3390/diagnostics13203267] [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: 10/03/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023] Open
Abstract
Artificial intelligence (AI) presents a novel platform for improving disease diagnosis. However, the clinical utility of AI remains limited to discovery studies, with poor translation to clinical practice. Current data suggests that 26% of diminutive pre-malignant lesions and 3.5% of colorectal cancers are missed during colonoscopies. The primary aim of this study was to explore the role of artificial intelligence in real-time histological prediction of colorectal lesions during colonoscopy. A systematic search using MeSH headings relating to "AI", "machine learning", "computer-aided", "colonoscopy", and "colon/rectum/colorectal" identified 2290 studies. Thirteen studies reporting real-time analysis were included. A total of 2958 patients with 5908 colorectal lesions were included. A meta-analysis of six studies reporting sensitivities (95% CI) demonstrated that endoscopist diagnosis was superior to a computer-assisted detection platform, although no statistical significance was reached (p = 0.43). AI applications have shown encouraging results in differentiating neoplastic and non-neoplastic lesions using narrow-band imaging, white light imaging, and blue light imaging. Other modalities include autofluorescence imaging and elastic scattering microscopy. The current literature demonstrates that despite the promise of new endoscopic AI models, they remain inferior to expert endoscopist diagnosis. There is a need to focus developments on real-time histological predictions prior to clinical translation to demonstrate improved diagnostic capabilities and time efficiency.
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Affiliation(s)
- Bhamini Vadhwana
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0HS, UK
| | - Munir Tarazi
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0HS, UK
| | - Vanash Patel
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0HS, UK
- West Hertfordshire Hospital NHS Trust, Vicarage Road, Watford WD18 0HB, UK
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27
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Sung JJY, Savulescu J, Ngiam KY, An B, Ang TL, Yeoh KG, Cham TJ, Tsao S, Chua TS. Artificial intelligence for gastroenterology: Singapore artificial intelligence for Gastroenterology Working Group Position Statement. J Gastroenterol Hepatol 2023; 38:1669-1676. [PMID: 37277693 DOI: 10.1111/jgh.16241] [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: 03/12/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 06/07/2023]
Abstract
BACKGROUND Successful implementation of artificial intelligence in gastroenterology and hepatology practice requires more than technology. There are ethical, legal, and social issues that need to be settled. AIM A group consisting of AI developers (engineer), AI users (gastroenterologist, hepatologist, and surgeon) and AI regulators (ethicist and administrator) formed a Working Group to draft these Positions Statements with the objective of arousing public and professional interest and dialogue, to promote ethical considerations when implementing AI technology, to suggest to policy makers and health authorities relevant factors to take into account when approving and regulating the use of AI tools, and to engage the profession in preparing for change in clinical practice. STATEMENTS These series of Position Statements point out the salient issues to maintain the trust between care provider and care receivers, and to legitimize the use of a non-human tool in healthcare delivery. It is based on fundamental principles such as respect, autonomy, privacy, responsibility, and justice. Enforcing the use of AI without considering these factor risk damaging the doctor-patient relationship.
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Affiliation(s)
- Joseph J Y Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Julian Savulescu
- Centre for Biomedical Ethics, National University of Singapore, Singapore
| | - K Y Ngiam
- Department of Surgery, National University Hospital, Singapore
| | - Bo An
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Tiing Leong Ang
- Singapore Health Service, Changi General Hospital, Singapore
| | - K G Yeoh
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Gastroenterology and Hepatology, National University Hospital, National University Health System, Singapore
| | - Tat-Jen Cham
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Stephen Tsao
- National Healthcare Group, Tan Tock Seng Hospital Singapore, Singapore
- Gastroenterological Society of Singapore, Singapore
| | - T S Chua
- Gastroenterology Chapter, Academy of Medicine, Singapore
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28
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Hassan C, Mori Y, Sharma P. The Pros and Cons of Artificial Intelligence in Endoscopy. Am J Gastroenterol 2023; 118:1720-1722. [PMID: 37052360 DOI: 10.14309/ajg.0000000000002287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 03/27/2023] [Indexed: 04/14/2023]
Affiliation(s)
- Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical and Research Center, IRCCS, Rozzano, Italy
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Prateek Sharma
- University of Kansas School of Medicine and VA Medical Center, Kansas City, Kansas, USA
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29
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Baumer S, Streicher K, Alqahtani SA, Brookman-Amissah D, Brunner M, Federle C, Muehlenberg K, Pfeifer L, Salzberger A, Schorr W, Zustin J, Pech O. Accuracy of polyp characterization by artificial intelligence and endoscopists: a prospective, non-randomized study in a tertiary endoscopy center. Endosc Int Open 2023; 11:E818-E828. [PMID: 37727511 PMCID: PMC10506867 DOI: 10.1055/a-2096-2960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/08/2023] [Indexed: 09/21/2023] Open
Abstract
Background and study aims Artificial intelligence (AI) in gastrointestinal endoscopy is developing very fast. Computer-aided detection of polyps and computer-aided diagnosis (CADx) for polyp characterization are available now. This study was performed to evaluate the diagnostic performance of a new commercially available CADx system in clinical practice. Patients and methods This prospective, non-randomized study was performed at a tertiary academic endoscopy center from March to August 2022. We included patients receiving a colonoscopy. Polypectomy had to be performed in all polyps. Every patient was examined concurrently by an endoscopist and AI using two opposing screens. The AI system, overseen by a second observer, was not visible to the endoscopist. The primary outcome was accuracy of the AI classifying the polyps into "neoplastic" and "non-neoplastic." The secondary outcome was accuracy of the classification by the endoscopists. Sessile serrated lesions were classified as neoplastic. Results We included 156 patients (mean age 65; 57 women) with 262 polyps ≤10 mm. Eighty-four were hyperplastic polyps (32.1%), 158 adenomas (60.3%), seven sessile serrated lesions (2.7%) and 13 other entities (normal/inflammatory colonmucosa, lymphoidic polyp) (4.9%) on histological diagnosis. Sensitivity, specificity and accuracy of AI were 89.70% (95% confidence interval [CI]: 84.02%-93.88%), 75.26% (95% CI: 65.46%-83.46%) and 84.35% (95% CI:79.38%-88.53%), respectively. Sensitivity, specificity and accuracy for less experienced endoscopists (2-5 years of endoscopy) were 95.56% (95% CI: 84.85%-99.46%), 61.54% (95% CI: 40.57%-79.77%) and 83.10% (95% CI: 72.34%-90.95%) and for experienced endoscopists 90.83% (95% CI: 84.19%-95.33%), 71.83% (95% CI: 59.90%-81.87%) and 83.77% (95% CI: 77.76%-88.70%), respectively. Conclusion Accuracy for polyp characterization by a new commercially available AI system is high, but does not fulfill the criteria for a "resect-and-discard" strategy.
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Affiliation(s)
- Sebastian Baumer
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Kilian Streicher
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Saleh A. Alqahtani
- Department of Gastroenterology and Hepatology, Johns Hopkins Hospital, Baltimore, United States
- Liver Transplant Center, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Dominic Brookman-Amissah
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Monika Brunner
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Christoph Federle
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Klaus Muehlenberg
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Lukas Pfeifer
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Andrea Salzberger
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Wolfgang Schorr
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Jozef Zustin
- Private Practice, Histopathology Service Private Practice, Regensburg, Germany
- Gerhard-Domagk-Institute of Pathology, Universitätsklinikum Münster, Munster, Germany
| | - Oliver Pech
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
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30
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Li JW, Wu CCH, Lee JWJ, Liang R, Soon GST, Wang LM, Koh XH, Koh CJ, Chew WD, Lin KW, Thian MY, Matthew R, Kim G, Khor CJL, Fock KM, Ang TL, So JBY. Real-World Validation of a Computer-Aided Diagnosis System for Prediction of Polyp Histology in Colonoscopy: A Prospective Multicenter Study. Am J Gastroenterol 2023; 118:1353-1364. [PMID: 37040553 DOI: 10.14309/ajg.0000000000002282] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/28/2023] [Indexed: 04/13/2023]
Abstract
INTRODUCTION Computer-aided diagnosis (CADx) of polyp histology could support endoscopists in clinical decision-making. However, this has not been validated in a real-world setting. METHODS We performed a prospective, multicenter study comparing CADx and endoscopist predictions of polyp histology in real-time colonoscopy. Optical diagnosis based on visual inspection of polyps was made by experienced endoscopists. After this, the automated output from the CADx support tool was recorded. All imaged polyps were resected for histological assessment. Primary outcome was difference in diagnostic performance between CADx and endoscopist prediction of polyp histology. Subgroup analysis was performed for polyp size, bowel preparation, difficulty of location of the polyps, and endoscopist experience. RESULTS A total of 661 eligible polyps were resected in 320 patients aged ≥40 years between March 2021 and July 2022. CADx had an overall accuracy of 71.6% (95% confidence interval [CI] 68.0-75.0), compared with 75.2% (95% CI 71.7-78.4) for endoscopists ( P = 0.023). The sensitivity of CADx for neoplastic polyps was 61.8% (95% CI 56.9-66.5), compared with 70.3% (95% CI 65.7-74.7) for endoscopists ( P < 0.001). The interobserver agreement between CADx and endoscopist predictions of polyp histology was moderate (83.1% agreement, κ 0.661). When there was concordance between CADx and endoscopist predictions, the accuracy increased to 78.1%. DISCUSSION The overall diagnostic accuracy and sensitivity for neoplastic polyps was higher in experienced endoscopists compared with CADx predictions, with moderate interobserver agreement. Concordance in predictions increased this diagnostic accuracy. Further research is required to improve the performance of CADx and to establish its role in clinical practice.
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Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
| | - Clement Chun Ho Wu
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
- Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore Health Services, Singapore
| | - Jonathan Wei Jie Lee
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, National University Health System, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Institute of Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore
| | - Raymond Liang
- Department of Gastroenterology and Hepatology, Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Gwyneth Shook Ting Soon
- Department of Pathology, National University Hospital, National University Health System, Singapore
| | - Lai Mun Wang
- Department of Laboratory Medicine, Changi General Hospital, Singapore Health Services, Singapore
| | - Xuan Han Koh
- Department of Health Sciences Research, Changi General Hospital, Singapore
| | - Calvin Jianyi Koh
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, National University Health System, Singapore
| | - Wei Da Chew
- Department of Gastroenterology and Hepatology, Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Kenneth Weicong Lin
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
| | - Mann Yie Thian
- Department of Gastroenterology and Hepatology, Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Ronnie Matthew
- Department of Colorectal Surgery, Singapore General Hospital, Singapore Health Services, Singapore
| | - Guowei Kim
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- University Surgical Cluster, National University Hospital, Singapore
| | - Christopher Jen Lock Khor
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
- Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore Health Services, Singapore
| | - Kwong Ming Fock
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
| | - Jimmy Bok Yan So
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- University Surgical Cluster, National University Hospital, Singapore
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van Bokhorst QNE, Houwen BBSL, Hazewinkel Y, Fockens P, Dekker E. Advances in artificial intelligence and computer science for computer-aided diagnosis of colorectal polyps: current status. Endosc Int Open 2023; 11:E752-E767. [PMID: 37593158 PMCID: PMC10431975 DOI: 10.1055/a-2098-1999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 05/08/2023] [Indexed: 08/19/2023] Open
Affiliation(s)
- Querijn N E van Bokhorst
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, location Academic Medical Center, Amsterdam, the Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, the Netherlands
| | - Britt B S L Houwen
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, location Academic Medical Center, Amsterdam, the Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, the Netherlands
| | - Yark Hazewinkel
- Department of Gastroenterology and Hepatology, Tergooi Medical Center, Hilversum, the Netherlands
| | - Paul Fockens
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, location Academic Medical Center, Amsterdam, the Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, the Netherlands
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, location Academic Medical Center, Amsterdam, the Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, the Netherlands
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32
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Houwen BBSL, Hazewinkel Y, Giotis I, Vleugels JLA, Mostafavi NS, van Putten P, Fockens P, Dekker E. Computer-aided diagnosis for optical diagnosis of diminutive colorectal polyps including sessile serrated lesions: a real-time comparison with screening endoscopists. Endoscopy 2023; 55:756-765. [PMID: 36623839 PMCID: PMC10374350 DOI: 10.1055/a-2009-3990] [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: 07/13/2022] [Accepted: 01/09/2023] [Indexed: 01/11/2023]
Abstract
BACKGROUND : We aimed to compare the accuracy of the optical diagnosis of diminutive colorectal polyps, including sessile serrated lesions (SSLs), between a computer-aided diagnosis (CADx) system and endoscopists during real-time colonoscopy. METHODS : We developed the POLyp Artificial Recognition (POLAR) system, which was capable of performing real-time characterization of diminutive colorectal polyps. For pretraining, the Microsoft-COCO dataset with over 300 000 nonpolyp object images was used. For training, eight hospitals prospectively collected 2637 annotated images from 1339 polyps (i. e. publicly available online POLAR database). For clinical validation, POLAR was tested during colonoscopy in patients with a positive fecal immunochemical test (FIT), and compared with the performance of 20 endoscopists from eight hospitals. Endoscopists were blinded to the POLAR output. Primary outcome was the comparison of accuracy of the optical diagnosis of diminutive colorectal polyps between POLAR and endoscopists (neoplastic [adenomas and SSLs] versus non-neoplastic [hyperplastic polyps]). Histopathology served as the reference standard. RESULTS : During clinical validation, 423 diminutive polyps detected in 194 FIT-positive individuals were included for analysis (300 adenomas, 41 SSLs, 82 hyperplastic polyps). POLAR distinguished neoplastic from non-neoplastic lesions with 79 % accuracy, 89 % sensitivity, and 38 % specificity. The endoscopists achieved 83 % accuracy, 92 % sensitivity, and 44 % specificity. The optical diagnosis accuracy between POLAR and endoscopists was not significantly different (P = 0.10). The proportion of polyps in which POLAR was able to provide an optical diagnosis was 98 % (i. e. success rate). CONCLUSIONS : We developed a CADx system that differentiated neoplastic from non-neoplastic diminutive polyps during endoscopy, with an accuracy comparable to that of screening endoscopists and near-perfect success rate.
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Affiliation(s)
- Britt B. S. L. Houwen
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Yark Hazewinkel
- Department of Gastroenterology and Hepatology, Radboud University Nijmegen Medical Center, Radboud University of Nijmegen, Nijmegen, The Netherlands
| | | | - Jasper L. A. Vleugels
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Nahid S. Mostafavi
- Department of Gastroenterology and Hepatology, Subdivision Statistics, Amsterdam University Medical Center, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Paul van Putten
- Department of Gastroenterology and Hepatology, Medical Center Leeuwarden, Leeuwarden, The Netherlands
| | - Paul Fockens
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands
- Bergman Clinics Maag and Darm Amsterdam, Amsterdam, The Netherlands
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33
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Rey JF. Artificial intelligence in digestive endoscopy: recent advances. Curr Opin Gastroenterol 2023:00001574-990000000-00089. [PMID: 37522929 DOI: 10.1097/mog.0000000000000957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
PURPOSE OF REVIEW With the incessant advances in information technology and its implications in all domains of our life, artificial intelligence (AI) started to emerge as a need for better machine performance. How it can help endoscopists and what are the areas of interest in improving both diagnostic and therapeutic endoscopy in each part of the gastrointestinal (GI) tract. What are the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. RECENT FINDINGS The two main AI systems categories are computer-assisted detection 'CADe' for lesion detection and computer-assisted diagnosis 'CADx' for optical biopsy and lesion characterization. Multiple softwares are now implemented in endoscopy practice. Other AI systems offer therapeutic assistance such as lesion delineation for complete endoscopic resection or prediction of possible lymphanode after endoscopic treatment. Quality assurance is the coming step with complete monitoring of high-quality colonoscopy. In all cases it is a computer-aid endoscopy as the overall result rely on the physician. Video capsule endoscopy is the unique example were the computer conduct the device, store multiple images, and perform accurate diagnosis. SUMMARY AI is a breakthrough in digestive endoscopy. Screening gastric and colonic cancer detection should be improved especially outside of expert's centers. Prospective and multicenter trials are mandatory before introducing new software in clinical practice.
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Affiliation(s)
- Jean-Francois Rey
- Arnault Tzanck Institute, 116 rue du commandant Cahuzac, Saint Laurent du var, France
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34
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Hassan C, Repici A, Sharma P. Incorporating Artificial Intelligence Into Gastroenterology Practices. Clin Gastroenterol Hepatol 2023; 21:1687-1689. [PMID: 36841445 DOI: 10.1016/j.cgh.2023.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
Affiliation(s)
- Cesare Hassan
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center - IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Alessandro Repici
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center - IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Prateek Sharma
- Division of Gastroenterology and Hepatology, VA Medical Center and University of Kansas School of Medicine, Kansas City, Kansas.
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35
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Galati JS, Lin K, Gross SA. Recent advances in devices and technologies that might prove revolutionary for colonoscopy procedures. Expert Rev Med Devices 2023; 20:1087-1103. [PMID: 37934873 DOI: 10.1080/17434440.2023.2280773] [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: 03/27/2023] [Accepted: 11/03/2023] [Indexed: 11/09/2023]
Abstract
INTRODUCTION Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR. AREAS COVERED The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies. EXPERT OPINION Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Kevin Lin
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY, USA
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36
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Mansour NM. Artificial Intelligence in Colonoscopy. Curr Gastroenterol Rep 2023; 25:122-129. [PMID: 37129831 DOI: 10.1007/s11894-023-00872-x] [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] [Accepted: 04/03/2023] [Indexed: 05/03/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is a rapidly growing field in gastrointestinal endoscopy, and its potential applications are virtually endless, with studies demonstrating use of AI for early gastric cancer, inflammatory bowel disease, Barrett's esophagus, capsule endoscopy, as well as other areas in gastroenterology. Much of the early studies and applications of AI in gastroenterology have revolved around colonoscopy, particularly with regards to real-time polyp detection and characterization. This review will cover much of the existing data on computer-aided detection (CADe), computer-aided diagnosis (CADx), and briefly discuss some other interesting applications of AI for colonoscopy, while also considering some of the challenges and limitations that exist around the use of AI for colonoscopy. RECENT FINDINGS Multiple randomized controlled trials have now been published which show a statistically significant improvement when using AI to improve adenoma detection and reduce adenoma miss rates during colonoscopy. There is also a growing pool of literature showing that AI can be helpful for characterizing/diagnosing colorectal polyps in real time. AI has also shown promise in other areas of colonoscopy, including polyp sizing and automated measurement and monitoring of quality metrics during colonoscopy. AI is a promising tool that has the ability to shape the future of gastrointestinal endoscopy, with much of the early data showing significant benefits to use of AI during colonoscopy. However, there remain several challenges that may delay or hamper the widespread use of AI in the field.
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Affiliation(s)
- Nabil M Mansour
- Section of Gastroenterology and Hepatology, Baylor College of Medicine, 7200 Cambridge St., Suite 8B, Houston, TX, 77030, USA.
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37
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Gimeno-García AZ, Hernández-Pérez A, Nicolás-Pérez D, Hernández-Guerra M. Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward? Cancers (Basel) 2023; 15:cancers15082193. [PMID: 37190122 DOI: 10.3390/cancers15082193] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/04/2023] [Accepted: 04/05/2023] [Indexed: 05/17/2023] Open
Abstract
Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement.
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Affiliation(s)
- Antonio Z Gimeno-García
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - Anjara Hernández-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - David Nicolás-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - Manuel Hernández-Guerra
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
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38
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Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening. Diagnostics (Basel) 2023; 13:diagnostics13061102. [PMID: 36980409 PMCID: PMC10047293 DOI: 10.3390/diagnostics13061102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/19/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.
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39
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Byrne MF, Von Renteln D, Barkun AN. Artificial Intelligence-Aided Colonoscopy for Characterizing and Detecting Colorectal Polyps: Required, Nice to Have, or Overhyped? Gastroenterology 2023; 164:332-333. [PMID: 36634825 DOI: 10.1053/j.gastro.2023.01.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 01/14/2023]
Affiliation(s)
- Michael F Byrne
- Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada
| | - Daniel Von Renteln
- Department of Medicine, Division of Gastroenterology, Montreal University Hospital Center, Montreal University Hospital Research Center, Université de Montréal, Montreal, Quebec, Canada
| | - Alan N Barkun
- Division of Gastroenterology, McGill University, McGill University Health Centre, Montreal, Quebec, Canada.
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40
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Mori Y, East JE, Hassan C, Halvorsen N, Berzin TM, Byrne M, von Renteln D, Hewett DG, Repici A, Ramchandani M, Al Khatry M, Kudo SE, Wang P, Yu H, Saito Y, Misawa M, Parasa S, Matsubayashi CO, Ogata H, Tajiri H, Pausawasdi N, Dekker E, Ahmad OF, Sharma P, Rex DK. Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement. Dig Endosc 2023; 35:422-429. [PMID: 36749036 DOI: 10.1111/den.14531] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 02/06/2023] [Indexed: 02/08/2023]
Abstract
The number of artificial intelligence (AI) tools for colonoscopy on the market is increasing with supporting clinical evidence. Nevertheless, their implementation is not going smoothly for a variety of reasons, including lack of data on clinical benefits and cost-effectiveness, lack of trustworthy guidelines, uncertain indications, and cost for implementation. To address this issue and better guide practitioners, the World Endoscopy Organization (WEO) has provided its perspective about the status of AI in colonoscopy as the position statement. WEO Position Statement: Statement 1.1: Computer-aided detection (CADe) for colorectal polyps is likely to improve colonoscopy effectiveness by reducing adenoma miss rates and thus increase adenoma detection; Statement 1.2: In the short term, use of CADe is likely to increase health-care costs by detecting more adenomas; Statement 1.3: In the long term, the increased cost by CADe could be balanced by savings in costs related to cancer treatment (surgery, chemotherapy, palliative care) due to CADe-related cancer prevention; Statement 1.4: Health-care delivery systems and authorities should evaluate the cost-effectiveness of CADe to support its use in clinical practice; Statement 2.1: Computer-aided diagnosis (CADx) for diminutive polyps (≤5 mm), when it has sufficient accuracy, is expected to reduce health-care costs by reducing polypectomies, pathological examinations, or both; Statement 2.2: Health-care delivery systems and authorities should evaluate the cost-effectiveness of CADx to support its use in clinical practice; Statement 3: We recommend that a broad range of high-quality cost-effectiveness research should be undertaken to understand whether AI implementation benefits populations and societies in different health-care systems.
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Affiliation(s)
- Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway.,Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway.,Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - James E East
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford, UK.,Division of Gastroenterology and Hepatology, Mayo Clinic Healthcare, London, UK
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.,Endoscopy Unit, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy
| | - Natalie Halvorsen
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
| | - Tyler M Berzin
- Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
| | - Michael Byrne
- Department of Medicine, The University of British Columbia, Vancouver, Canada
| | - Daniel von Renteln
- Division of Gastroenterology, University of Montreal Medical Center (CHUM) and Research Center (CRCHUM), Montreal, Canada
| | - David G Hewett
- School of Medicine, The University of Queensland, Brisbane, Australia
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.,Endoscopy Unit, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy
| | | | - Maryam Al Khatry
- Department of Gastroenterology, Obaidulla Hospital, Ras Al Khaimah, United Arab Emirates
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Pu Wang
- Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | | | - Carolina Ogawa Matsubayashi
- Gastrointestinal Endoscopy Unit, Gastroenterology Department, University of São Paulo Medical School, São Paulo, Brazil
| | - Haruhiko Ogata
- Center for Diagnostic and Therapeutic Endoscopy, School of Medicine, Keio University, Tokyo, Japan
| | - Hisao Tajiri
- Jikei University School of Medicine, Tokyo, Japan
| | - Nonthalee Pausawasdi
- Vikit Viranuvatti Siriraj GI Endoscopy Center,, Mahidol University, Bangkok, Thailand.,Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | | | - Prateek Sharma
- Division of Gastroenterology and Hepatology, University of Kansas School of Medicine and VA Medical Center, Kansas City, USA
| | - Douglas K Rex
- Division of Gastroenterology, Indiana University School of Medicine, Indianapolis, USA
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41
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Murino A, Rimondi A. Automated artificial intelligence scoring systems for the endoscopic assessment of ulcerative colitis: How far are we from clinical application? Gastrointest Endosc 2023; 97:347-349. [PMID: 36509572 DOI: 10.1016/j.gie.2022.10.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 10/04/2022] [Indexed: 12/15/2022]
Affiliation(s)
- Alberto Murino
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead; Department of Gastroenterology, Cleveland Clinic London, London, United Kingdom
| | - Alessandro Rimondi
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead; Department of Gastroenterology, Cleveland Clinic London, London, United Kingdom; Department of Pathophysiology and Transplantation, University of Milan, Italy, Milan, Italy; Center for Prevention and Diagnosis of Celiac Disease and Division of Gastroenterology and Endoscopy, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
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42
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Da Rio L, Spadaccini M, Parigi TL, Gabbiadini R, Dal Buono A, Busacca A, Maselli R, Fugazza A, Colombo M, Carrara S, Franchellucci G, Alfarone L, Facciorusso A, Hassan C, Repici A, Armuzzi A. Artificial intelligence and inflammatory bowel disease: Where are we going? World J Gastroenterol 2023; 29:508-520. [PMID: 36688019 PMCID: PMC9850939 DOI: 10.3748/wjg.v29.i3.508] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/05/2022] [Accepted: 12/28/2022] [Indexed: 01/12/2023] Open
Abstract
Inflammatory bowel diseases, namely ulcerative colitis and Crohn’s disease, are chronic and relapsing conditions that pose a growing burden on healthcare systems worldwide. Because of their complex and partly unknown etiology and pathogenesis, the management of ulcerative colitis and Crohn’s disease can prove challenging not only from a clinical point of view but also for resource optimization. Artificial intelligence, an umbrella term that encompasses any cognitive function developed by machines for learning or problem solving, and its subsets machine learning and deep learning are becoming ever more essential tools with a plethora of applications in most medical specialties. In this regard gastroenterology is no exception, and due to the importance of endoscopy and imaging numerous clinical studies have been gradually highlighting the relevant role that artificial intelligence has in inflammatory bowel diseases as well. The aim of this review was to summarize the most recent evidence on the use of artificial intelligence in inflammatory bowel diseases in various contexts such as diagnosis, follow-up, treatment, prognosis, cancer surveillance, data collection, and analysis. Moreover, insights into the potential further developments in this field and their effects on future clinical practice were discussed.
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Affiliation(s)
- Leonardo Da Rio
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Tommaso Lorenzo Parigi
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Roberto Gabbiadini
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Arianna Dal Buono
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Anita Busacca
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Alessandro Fugazza
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Matteo Colombo
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Silvia Carrara
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Gianluca Franchellucci
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Ludovico Alfarone
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Medical Sciences, University of Foggia, Foggia 71122, Foggia, Italy
| | - Cesare Hassan
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Alessandro Armuzzi
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
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Ahmad OF. Artificial intelligence for polyp characterization: easy as ABC. Endoscopy 2023; 55:23-24. [PMID: 36162423 DOI: 10.1055/a-1931-4332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Omer F Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.,Gastrointestinal Services, University College London Hospital, London, UK
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Messmann H, Bisschops R, Antonelli G, Libânio D, Sinonquel P, Abdelrahim M, Ahmad OF, Areia M, Bergman JJGHM, Bhandari P, Boskoski I, Dekker E, Domagk D, Ebigbo A, Eelbode T, Eliakim R, Häfner M, Haidry RJ, Jover R, Kaminski MF, Kuvaev R, Mori Y, Palazzo M, Repici A, Rondonotti E, Rutter MD, Saito Y, Sharma P, Spada C, Spadaccini M, Veitch A, Gralnek IM, Hassan C, Dinis-Ribeiro M. Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2022; 54:1211-1231. [PMID: 36270318 DOI: 10.1055/a-1950-5694] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
This ESGE Position Statement defines the expected value of artificial intelligence (AI) for the diagnosis and management of gastrointestinal neoplasia within the framework of the performance measures already defined by ESGE. This is based on the clinical relevance of the expected task and the preliminary evidence regarding artificial intelligence in artificial or clinical settings. MAIN RECOMMENDATIONS:: (1) For acceptance of AI in assessment of completeness of upper GI endoscopy, the adequate level of mucosal inspection with AI should be comparable to that assessed by experienced endoscopists. (2) For acceptance of AI in assessment of completeness of upper GI endoscopy, automated recognition and photodocumentation of relevant anatomical landmarks should be obtained in ≥90% of the procedures. (3) For acceptance of AI in the detection of Barrett's high grade intraepithelial neoplasia or cancer, the AI-assisted detection rate for suspicious lesions for targeted biopsies should be comparable to that of experienced endoscopists with or without advanced imaging techniques. (4) For acceptance of AI in the management of Barrett's neoplasia, AI-assisted selection of lesions amenable to endoscopic resection should be comparable to that of experienced endoscopists. (5) For acceptance of AI in the diagnosis of gastric precancerous conditions, AI-assisted diagnosis of atrophy and intestinal metaplasia should be comparable to that provided by the established biopsy protocol, including the estimation of extent, and consequent allocation to the correct endoscopic surveillance interval. (6) For acceptance of artificial intelligence for automated lesion detection in small-bowel capsule endoscopy (SBCE), the performance of AI-assisted reading should be comparable to that of experienced endoscopists for lesion detection, without increasing but possibly reducing the reading time of the operator. (7) For acceptance of AI in the detection of colorectal polyps, the AI-assisted adenoma detection rate should be comparable to that of experienced endoscopists. (8) For acceptance of AI optical diagnosis (computer-aided diagnosis [CADx]) of diminutive polyps (≤5 mm), AI-assisted characterization should match performance standards for implementing resect-and-discard and diagnose-and-leave strategies. (9) For acceptance of AI in the management of polyps ≥ 6 mm, AI-assisted characterization should be comparable to that of experienced endoscopists in selecting lesions amenable to endoscopic resection.
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Affiliation(s)
- Helmut Messmann
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli Hospital, Ariccia, Rome, Italy
- Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Italy
| | - Diogo Libânio
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal
- MEDCIDS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Pieter Sinonquel
- Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
| | - Mohamed Abdelrahim
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Omer F Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK
- Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
- Gastrointestinal Services, University College London Hospital, London, UK
| | - Miguel Areia
- Gastroenterology Department, Portuguese Oncology Institute of Coimbra, Coimbra, Portugal
| | | | - Pradeep Bhandari
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Ivo Boskoski
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Dirk Domagk
- Department of Medicine I, Josephs-Hospital Warendorf, Academic Teaching Hospital, University of Muenster, Warendorf, Germany
| | - 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
| | - Rami Eliakim
- Department of Gastroenterology, Sheba Medical Center Tel Hashomer & Sackler School of Medicine, Tel-Aviv University, Ramat Gan, Israel
| | - Michael Häfner
- 2nd Medical Department, Barmherzige Schwestern Krankenhaus, Vienna, Austria
| | - Rehan J Haidry
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK
- Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
| | - Rodrigo Jover
- Servicio de Gastroenterología, Hospital General Universitario Dr. Balmis, Instituto de Investigación Biomédica de Alicante ISABIAL, Departamento de Medicina Clínica, Universidad Miguel Hernández, Alicante, Spain
| | - Michal F Kaminski
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Department of Gastroenterology, Hepatology and Clinical Oncology, Centre of Postgraduate Medical Education, Warsaw, Poland
- Department of Oncological Gastroenterology and Department of Cancer Prevention, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Roman Kuvaev
- Endoscopy Department, Yaroslavl Regional Cancer Hospital, Yaroslavl, Russian Federation
- Department of Gastroenterology, Faculty of Additional Professional Education, N.A. Pirogov Russian National Research Medical University, Moscow, Russian Federation
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | | | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | | | - Matthew D Rutter
- North Tees and Hartlepool NHS Foundation Trust, Stockton-on-Tees, UK
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Prateek Sharma
- Gastroenterology and Hepatology Division, University of Kansas School of Medicine, Kansas, USA
- Kansas City VA Medical Center, Kansas City, USA
| | - Cristiano Spada
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Digestive Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Andrew Veitch
- Department of Gastroenterology, Royal Wolverhampton Hospitals NHS Trust, Wolverhampton, UK
| | - Ian M Gralnek
- Ellen and Pinchas Mamber Institute of Gastroenterology and Hepatology, Emek Medical Center, Afula, Israel
- Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Mario Dinis-Ribeiro
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal
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von Renteln D, Byrne MF, Wong C, Menard C, Donnellan F, Barkun A. Implementation of Artificial Intelligence-Assisted Endoscopy Across Canada-The CAG Artificial Intelligence Special Interest Group. J Can Assoc Gastroenterol 2022; 6:5-7. [PMID: 36789145 PMCID: PMC9915053 DOI: 10.1093/jcag/gwac030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Daniel von Renteln
- Correspondence: Daniel von Renteln, MD, Department of Medicine, Division of Gastroenterology, Montreal University Hospital Center (CHUM) and Montreal University Hospital Research Center (CRCHUM), 900 rue St-Denis, Montreal, Quebec H2X 0A9, Canada, e-mail:
| | - Michael F Byrne
- Division of Gastroenterology, Vancouver General Hospital and University of British Columbia, Vancouver, British Columbia, Canada
| | - Clarence Wong
- Division of Gastroenterology, Royal Alexandra Hospital, University of Alberta, Edmonton, Alberta, Canada
| | - Charles Menard
- Division of Gastroenterology, University of Sherbrooke, Sherbrooke, Quebec, Canada
| | - Fergal Donnellan
- Division of Gastroenterology, Vancouver General Hospital and University of British Columbia, Vancouver, British Columbia, Canada
| | - Alan Barkun
- Division of Gastroenterology and Hepatology, McGill University Health Centre, McGill University, Montreal, Quebec, Canada
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