Ishita Barua1, Daniela Guerrero Vinsard2,3, Henriette C Jodal1, Magnus Løberg1, Mette Kalager1, Øyvind Holme1, Masashi Misawa4, Michael Bretthauer1, Yuichi Mori1,4. 1. Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, and Department of Transplantation Medicine Oslo University Hospital, Oslo, Norway. 2. Department of Internal Medicine, University of Connecticut Health Centre, Connecticut, USA. 3. Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA. 4. Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.
Abstract
BACKGROUND: Artificial intelligence (AI)-based polyp detection systems are used during colonoscopy with the aim of increasing lesion detection and improving colonoscopy quality. PATIENTS AND METHODS: We performed a systematic review and meta-analysis of prospective trials to determine the value of AI-based polyp detection systems for detection of polyps and colorectal cancer. We performed systematic searches in MEDLINE, EMBASE, and Cochrane CENTRAL. Independent reviewers screened studies and assessed eligibility, certainty of evidence, and risk of bias. We compared colonoscopy with and without AI by calculating relative and absolute risks and mean differences for detection of polyps, adenomas, and colorectal cancer. RESULTS: Five randomized trials were eligible for analysis. Colonoscopy with AI increased adenoma detection rates (ADRs) and polyp detection rates (PDRs) compared to colonoscopy without AI (values given with 95 %CI). ADR with AI was 29.6 % (22.2 % - 37.0 %) versus 19.3 % (12.7 % - 25.9 %) without AI; relative risk (RR] 1.52 (1.31 - 1.77), with high certainty. PDR was 45.4 % (41.1 % - 49.8 %) with AI versus 30.6 % (26.5 % - 34.6 %) without AI; RR 1.48 (1.37 - 1.60), with high certainty. There was no difference in detection of advanced adenomas (mean advanced adenomas per colonoscopy 0.03 for each group, high certainty). Mean adenomas detected per colonoscopy was higher for small adenomas (≤ 5 mm) for AI versus non-AI (mean difference 0.15 [0.12 - 0.18]), but not for larger adenomas (> 5 - ≤ 10 mm, mean difference 0.03 [0.01 - 0.05]; > 10 mm, mean difference 0.01 [0.00 - 0.02]; high certainty). Data on cancer are unavailable. CONCLUSIONS: AI-based polyp detection systems during colonoscopy increase detection of small nonadvanced adenomas and polyps, but not of advanced adenomas. Thieme. All rights reserved.
BACKGROUND: Artificial intelligence (AI)-based polyp detection systems are used during colonoscopy with the aim of increasing lesion detection and improving colonoscopy quality. PATIENTS AND METHODS: We performed a systematic review and meta-analysis of prospective trials to determine the value of AI-based polyp detection systems for detection of polyps and colorectal cancer. We performed systematic searches in MEDLINE, EMBASE, and Cochrane CENTRAL. Independent reviewers screened studies and assessed eligibility, certainty of evidence, and risk of bias. We compared colonoscopy with and without AI by calculating relative and absolute risks and mean differences for detection of polyps, adenomas, and colorectal cancer. RESULTS: Five randomized trials were eligible for analysis. Colonoscopy with AI increased adenoma detection rates (ADRs) and polyp detection rates (PDRs) compared to colonoscopy without AI (values given with 95 %CI). ADR with AI was 29.6 % (22.2 % - 37.0 %) versus 19.3 % (12.7 % - 25.9 %) without AI; relative risk (RR] 1.52 (1.31 - 1.77), with high certainty. PDR was 45.4 % (41.1 % - 49.8 %) with AI versus 30.6 % (26.5 % - 34.6 %) without AI; RR 1.48 (1.37 - 1.60), with high certainty. There was no difference in detection of advanced adenomas (mean advanced adenomas per colonoscopy 0.03 for each group, high certainty). Mean adenomas detected per colonoscopy was higher for small adenomas (≤ 5 mm) for AI versus non-AI (mean difference 0.15 [0.12 - 0.18]), but not for larger adenomas (> 5 - ≤ 10 mm, mean difference 0.03 [0.01 - 0.05]; > 10 mm, mean difference 0.01 [0.00 - 0.02]; high certainty). Data on cancer are unavailable. CONCLUSIONS: AI-based polyp detection systems during colonoscopy increase detection of small nonadvanced adenomas and polyps, but not of advanced adenomas. Thieme. All rights reserved.
Authors: Babu P Mohan; Antonio Facciorusso; Shahab R Khan; Saurabh Chandan; Lena L Kassab; Paraskevas Gkolfakis; Georgios Tziatzios; Konstantinos Triantafyllou; Douglas G Adler Journal: EClinicalMedicine Date: 2020-11-21
Authors: Ulrik Deding; Anders Høgh; Niels Buch; Anastasios Koulaouzidis; Gunnar Baatrup; Thomas Bjørsum-Meyer Journal: Endosc Int Open Date: 2021-11-12
Authors: Omer F Ahmad; Yuichi Mori; Masashi Misawa; Shin-Ei Kudo; John T Anderson; Jorge Bernal; Tyler M Berzin; Raf Bisschops; Michael F Byrne; Peng-Jen Chen; James E East; Tom Eelbode; Daniel S Elson; Suryakanth R Gurudu; Aymeric Histace; William E Karnes; Alessandro Repici; Rajvinder Singh; Pietro Valdastri; Michael B Wallace; Pu Wang; Danail Stoyanov; Laurence B Lovat Journal: Endoscopy Date: 2021-01-13 Impact factor: 9.776