Machine learning in GI endoscopy: practical guidance in how to interpret a novel field
Fons van der Sommen, Jeroen de Groof, Maarten R. Struyvenberg, Joost van der Putten, Tim Boers, Kiki Fockens, Erik J. Schoon, Wouter L. Curvers, Peter de With, Yuichi Mori, Michael F. Byrne, Jacques Bergman
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.