Peer-Reviewed Publication
Gastrointest Endosc2025;102(1):109-116.e2.July 1, 2025Journal Article
Creating a standardized tool for the evaluation and comparison of artificial intelligence-based computer-aided detection programs in colonoscopy: a modified Delphi approach.
Sanjay R V Gadi1, Yuichi Mori2, Masashi Misawa3, James E East4, Cesare Hassan5, Alessandro Repici5, Michael F Byrne6, Daniel von Renteln7, David G Hewett8, Pu Wang9, Yutaka Saito10, Carolina Ogawa Matsubayashi11, Omer F Ahmad12, Prateek Sharma13, Seth A Gross14, Neil Sengupta15, Nabil Mansour16, Andrea Cherubini17, Nhan Ngo Dinh17, Xiao Xiao18, Peter Mountney19, Juana González-Bueno Puyal19, Greg Little20, Shawn LaRocco20, Sailesh Conjeti20, Hannes Seibt21, Dror Zur22, Hitoshi Shimada23, Tyler M Berzin24, Jeremy R Glissen Brown25
1Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA. Electronic address: sanjay.gadi@duke.edu.
2Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan; Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway.
3Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.
4Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, United Kingdom; Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, United Kingdom.
5Department of Biomedical Sciences Humanitas University, Pieve Emanuele, Milan, Italy; IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.
6Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada; Satisfai Health, Vancouver, British Columbia, Canada.
7Division of Gastroenterology, Montréal University Hospital and Research Center, Montréal, Québec, Canada.
8School of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
9Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
10Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan.
11Gastrointestinal Endoscopy Unit, Gastroenterology Department, University of São Paulo Medical School, São Paulo, Brazil; AI Medical Services Inc., Tokyo, Japan.
12Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, United Kingdom.
13Division of Gastroenterology and Hepatology, University of Kansas School of Medicine and VA Medical Center, Kansas City, Kansas, USA.
14Division of Gastroenterology and Hepatology, New York University Langone Health System, New York, New York, USA.
15Section of Gastroenterology, University of Chicago Medicine, Chicago, Illinois, USA.
16Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, Texas, USA.
17Cosmo Intelligent Medical Devices, Dublin, Ireland.
18Wision AI, Palo Alto, California, USA.
19Odin Vision, London, United Kingdom; Olympus Corporation, Tokyo, Japan.
20Olympus Corporation, Tokyo, Japan.
21Pentax Medical Europe, Hamburg, Germany.
22Magentiq Eye, Haifa, Israel.
23FUJIFILM Healthcare Americas Corporation, Lexington, Massachusetts, USA.
24Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA.
25Division of Gastroenterology, Duke University Medical Center, Durham, North Carolina, USA.
Abstract
BACKGROUND AND AIMS: Multiple computer-aided detection (CADe) software programs have now achieved regulatory approval in the United States, Europe, and Asia and are being used in routine clinical practice to support colorectal cancer screening. There is uncertainty regarding how different CADe algorithms may perform. No objective methodology exists for comparing different algorithms. We aimed to i…
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