RT Journal Article
SR Electronic
T1 Machine learning in GI endoscopy: practical guidance in how to interpret a novel field
JF Gut
JO Gut
FD BMJ Publishing Group Ltd and British Society of Gastroenterology
SP 2035
OP 2045
DO 10.1136/gutjnl-2019-320466
VO 69
IS 11
A1 Fons van der Sommen
A1 Jeroen de Groof
A1 Maarten Struyvenberg
A1 Joost van der Putten
A1 Tim Boers
A1 Kiki Fockens
A1 Erik J Schoon
A1 Wouter Curvers
A1 Peter de With
A1 Yuichi Mori
A1 Michael Byrne
A1 Jacques J G H M Bergman
YR 2020
UL http://gut.bmj.com/content/69/11/2035.abstract
AB 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.