Artificial intelligence in small bowel capsule endoscopy ‐ current status, challenges and future promise

• Published in: Journal of gastroenterology and hepatology Vol. 36; no. 1; pp. 12 - 19
• Main Authors: Dray, Xavier, Iakovidis, Dimitris, Houdeville, Charles, Jover, Rodrigo, Diamantis, Dimitris, Histace, Aymeric, Koulaouzidis, Anastasios
• Format: Journal Article
• Language: English
• Published: Australia Wiley Subscription Services, Inc 01.01.2021; Wiley
• Subjects: algorithms; Artificial intelligence; capsule endoscopy; Computer Science; deep learning; Endoscopy; Engineering Sciences; Human health and pathology; Hépatology and Gastroenterology; Image Processing; Learning algorithms; Life Sciences; Machine learning; Neural networks; Signal and Image processing; small bowel; Small intestine; algorithms; deep learning; small bowel; artificial intelligence; capsule endoscopy; neural networks
• ISSN: 0815-9319; 1440-1746
• DOI: 10.1111/jgh.15341

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.