Machine learning in GI endoscopy: practical guidance in how to interpret a novel field

Fons van der Sommen, Jeroen de Groof, Maarten Struyvenberg, Joost van der Putten, Tim Boers, Kiki Fockens, Erik J Schoon, Wouter Curvers, Peter de With, Yuichi Mori, Michael Byrne, Jacques J G H M Bergman (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2035-2045
Number of pages11
JournalGut
Volume69
Issue number11
Early online date11 May 2020
DOIs
Publication statusPublished - Nov 2020

Keywords

  • computerised image analysis
  • endoscopy
  • gastrointesinal endoscopy

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