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Artificial intelligence in small bowel capsule endoscopy ‐ current status, challenges and future promise
Author(s) -
Dray Xavier,
Iakovidis Dimitris,
Houdeville Charles,
Jover Rodrigo,
Diamantis Dimitris,
Histace Aymeric,
Koulaouzidis Anastasios
Publication year - 2021
Publication title -
journal of gastroenterology and hepatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.214
H-Index - 130
eISSN - 1440-1746
pISSN - 0815-9319
DOI - 10.1111/jgh.15341
Subject(s) - medicine , capsule endoscopy , task (project management) , artificial intelligence , reading (process) , endoscopy , medical physics , machine learning , computer science , radiology , political science , law , economics , management
Neural network‐based solutions are under development to alleviate physicians from the tedious task of small‐bowel capsule endoscopy reviewing. Computer‐assisted detection is a critical step, aiming to reduce reading times while maintaining accuracy. Weakly supervised solutions have shown promising results; however, video‐level evaluations are scarce, and no prospective studies have been conducted yet. Automated characterization (in terms of diagnosis and pertinence) by supervised machine learning solutions is the next step. It relies on large, thoroughly labeled databases, for which preliminary “ground truth” definitions by experts are of tremendous importance. Other developments are under ways, to assist physicians in localizing anatomical landmarks and findings in the small bowel, in measuring lesions, and in rating bowel cleanliness. It is still questioned whether artificial intelligence will enter the market with proprietary, built‐in or plug‐in software, or with a universal cloud‐based service, and how it will be accepted by physicians and patients.