Toward automated small bowel capsule endoscopy reporting using a summarizing machine learning algorithm: The SUM UP study.

Journal: Clinics and research in hepatology and gastroenterology
PMID: 39622290

Abstract

BACKGROUND AND OBJECTIVES: Deep learning (DL) algorithms demonstrate excellent diagnostic performance for the detection of vascular lesions via small bowel (SB) capsule endoscopy (CE), including vascular abnormalities with high (P2), intermediate (P1) or low (P0) bleeding potential, while dramatically decreasing the reading time. We aimed to improve the performance of a DL algorithm by characterizing vascular abnormalities using a machine learning (ML) classifier, and selecting the most relevant images for insertion into reports.

Authors

  • Charles Houdeville
    Sorbonne Université, Centre d'Endoscopie Digestive, Hôpital Saint-Antoine, APHP, Paris, France.
  • Marc Souchaud
    ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS), Cergy, France.
  • Romain Leenhardt
    Sorbonne University, Department of Hepato-Gastroenterology, APHP, Saint Antoine Hospital, Paris, France.
  • Lia Cmj Goltstein
    Department of Gastroenterology and Hepatology, Radboud University Medical Center, Nijmegen, Netherlands.
  • Guillaume Velut
    Sorbonne University, Center for Digestive Endoscopy, Saint-Antoine Hospital, APHP, 75012 Paris, France; Department of Gastroenterology CHU Nantes, Hotel Dieu, Nantes, France.
  • Hanneke Beaumont
    Amsterdam Universitair Medische Centra, Amsterdam, Netherlands.
  • Xavier Dray
    Sorbonne University, Department of Hepato-Gastroenterology, APHP, Saint Antoine Hospital, Paris, France; ETIS, Université de Cergy-Pontoise, ENSEA, CNRS, Cergy-Pontoise Cedex, France.
  • Aymeric Histace
    ETIS, Université de Cergy-Pontoise, ENSEA, CNRS, Cergy-Pontoise Cedex, France.

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