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

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  • Xavier Dray
    Sorbonne Université, Centre d'Endoscopie Digestive Hôpital Saint‐Antoine, APHP Paris France
  • Dimitris Iakovidis
    Department of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece
  • Charles Houdeville
    Sorbonne Université, Centre d'Endoscopie Digestive Hôpital Saint‐Antoine, APHP Paris France
  • Rodrigo Jover
    Servicio de Medicina Digestiva Hospital General Universitario de Alicante, Instituto de Investigación Biomédica ISABIAL Alicante Spain
  • Dimitris Diamantis
    Department of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece
  • Aymeric Histace
    ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS) Cergy France
  • Anastasios Koulaouzidis
    Endoscopy Unit The Royal Infirmary of Edinburgh Edinburgh UK

Description

<jats:title>Abstract</jats:title><jats:p>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.</jats:p>

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