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Machine learning in GI endoscopy: practical guidance in how to interpret a novel field
  1. Fons van der Sommen1,
  2. Jeroen de Groof2,
  3. Maarten Struyvenberg2,
  4. Joost van der Putten1,
  5. Tim Boers1,
  6. Kiki Fockens2,
  7. Erik J Schoon3,
  8. Wouter Curvers3,
  9. Peter de With1,
  10. Yuichi Mori4,
  11. Michael Byrne5,
  12. Jacques J G H M Bergman2
  1. 1 Department of Electrical Engineering, VCA Group, University of Technology Eindhoven, Eindhoven, Noord-Brabant, The Netherlands
  2. 2 Department of Gastroenterology and Hepatology, Amsterdam UMC—Locatie AMC, Amsterdam, North Holland, The Netherlands
  3. 3 Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, The Netherlands
  4. 4 Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
  5. 5 Division of Gastroenterology, Vancouver General Hospital, The University of British Columbia, Vancouver, British Columbia, Canada
  1. Correspondence to Dr Jacques J G H M Bergman, Department of Gastroenterology and Hepatology, Amsterdam UMC - Locatie AMC, Amsterdam 1105 AZ, The Netherlands; j.j.bergman{at}


There has been a vast increase in GI literature focused on the use of machine learning in endoscopy. The relative novelty of this field poses a challenge for reviewers and readers of GI journals. To appreciate scientific quality and novelty of machine learning studies, understanding of the technical basis and commonly used techniques is required. Clinicians often lack this technical background, while machine learning experts may be unfamiliar with clinical relevance and implications for daily practice. Therefore, there is an increasing need for a multidisciplinary, international evaluation on how to perform high-quality machine learning research in endoscopy. This review aims to provide guidance for readers and reviewers of peer-reviewed GI journals to allow critical appraisal of the most relevant quality requirements of machine learning studies. The paper provides an overview of common trends and their potential pitfalls and proposes comprehensive quality requirements in six overarching themes: terminology, data, algorithm description, experimental setup, interpretation of results and machine learning in clinical practice.

  • endoscopy
  • gastrointesinal endoscopy
  • computerised image analysis

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  • FvdS and JdG are joint first authors.

  • Twitter @FvdSommen

  • FvdS and JdG contributed equally.

  • Correction notice This article has been corrected since it published Online First. An acknowledgement has been added to legend for figure 4.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.