CC BY 4.0 · Endoscopy
DOI: 10.1055/a-1306-7590
Original article

Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method

 1  Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK
,
 2  Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
 3  Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway
,
Masashi Misawa
 2  Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
 2  Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
 4  Department of Gastroenterology, Gloucestershire Hospitals NHS Foundation Trust, Gloucester, UK
,
Jorge Bernal
 5  Computer Science Department, Universitat Autonoma de Barcelona and Computer Vision Center, Barcelona, Spain
,
Tyler M. Berzin
 6  Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
,
 7  Department of Gastroenterology and Hepatology, University Hospitals Leuven, TARGID KU Leuven, Leuven, Belgium
,
Michael F. Byrne
 8  Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada
,
 9  Division of Gastroenterology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
,
10  Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, UK
11  Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
,
Tom Eelbode
12  Medical Imaging Research Center, ESAT/PSI, KU Leuven, Leuven, Belgium
,
Daniel S. Elson
13  Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, London, UK
14  Department of Surgery and Cancer, Imperial College London, London, UK
,
Suryakanth R. Gurudu
15  Division of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, Arizona, USA
,
16  ETIS, Universite de Cergy-Pointoise, ENSEA, CNRS, Cergy-Pointoise Cedex, France
,
William E. Karnes
17  H. H. Chao Comprehensive Digestive Disease Center, Division of Gastroenterology & Hepatology, Department of Medicine, University of California, Irvine, California, USA
,
Alessandro Repici
18  Department of Gastroenterology, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy
19  Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Milan, Italy
,
Rajvinder Singh
20  Department of Gastroenterology and Hepatology, Lyell McEwan Hospital, Adelaide, South Australia, Australia
,
Pietro Valdastri
21  School of Electronics and Electrical Engineering, University of Leeds, Leeds, UK
,
22  Division of Gastroenterology & Hepatology, Mayo Clinic, Jacksonville, Florida, USA
,
23  Department of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, China
,
Danail Stoyanov
 1  Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK
,
Laurence B. Lovat
 1  Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK
24  Gastrointestinal Services, University College London Hospital, London, UK
› Author Affiliations
Supported by: Wellcome Trust 203145Z/16/Z
Supported by: Engineering and Physical Sciences Research Council 203145Z/16/Z EP/P027938/1

Abstract

Background Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities.

Methods An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions.

Results The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient.

Conclusions This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.

Appendix 1s, 2s, Table 1s



Publication History

Received: 24 June 2020

Accepted: 09 November 2020

Publication Date:
09 November 2020 (online)

© 2020. © 2020. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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