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Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method.
Ahmad, Omer F; Mori, Yuichi; Misawa, Masashi; Kudo, Shin-Ei; Anderson, John T; Bernal, Jorge; Berzin, Tyler M; Bisschops, Raf; Byrne, Michael F; Chen, Peng-Jen; East, James E; Eelbode, Tom; Elson, Daniel S; Gurudu, Suryakanth R; Histace, Aymeric; Karnes, William E; Repici, Alessandro; Singh, Rajvinder; Valdastri, Pietro; Wallace, Michael B; Wang, Pu; Stoyanov, Danail; Lovat, Laurence B.
Afiliação
  • Ahmad OF; Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK.
  • Mori Y; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.
  • Misawa M; Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway.
  • Kudo SE; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.
  • Anderson JT; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.
  • Bernal J; Department of Gastroenterology, Gloucestershire Hospitals NHS Foundation Trust, Gloucester, UK.
  • Berzin TM; Computer Science Department, Universitat Autonoma de Barcelona and Computer Vision Center, Barcelona, Spain.
  • Bisschops R; Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
  • Byrne MF; Department of Gastroenterology and Hepatology, University Hospitals Leuven, TARGID KU Leuven, Leuven, Belgium.
  • Chen PJ; Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada.
  • East JE; Division of Gastroenterology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
  • Eelbode T; Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, UK.
  • Elson DS; Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK.
  • Gurudu SR; Medical Imaging Research Center, ESAT/PSI, KU Leuven, Leuven, Belgium.
  • Histace A; Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, London, UK.
  • Karnes WE; Department of Surgery and Cancer, Imperial College London, London, UK.
  • Repici A; Division of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, Arizona, USA.
  • Singh R; ETIS, Universite de Cergy-Pointoise, ENSEA, CNRS, Cergy-Pointoise Cedex, France.
  • Valdastri P; H. H. Chao Comprehensive Digestive Disease Center, Division of Gastroenterology & Hepatology, Department of Medicine, University of California, Irvine, California, USA.
  • Wallace MB; Department of Gastroenterology, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy.
  • Wang P; Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Milan, Italy.
  • Stoyanov D; Department of Gastroenterology and Hepatology, Lyell McEwan Hospital, Adelaide, South Australia, Australia.
  • Lovat LB; School of Electronics and Electrical Engineering, University of Leeds, Leeds, UK.
Endoscopy ; 53(9): 893-901, 2021 09.
Article em En | MEDLINE | ID: mdl-33167043
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Colonoscopia Tipo de estudo: Prognostic_studies / Qualitative_research Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Colonoscopia Tipo de estudo: Prognostic_studies / Qualitative_research Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article