Deep learning for automatic bowel-obstruction identification on abdominal CT.

Journal: European radiology
PMID: 38388719

Abstract

RATIONALE AND OBJECTIVES: Automated evaluation of abdominal computed tomography (CT) scans should help radiologists manage their massive workloads, thereby leading to earlier diagnoses and better patient outcomes. Our objective was to develop a machine-learning model capable of reliably identifying suspected bowel obstruction (BO) on abdominal CT.

Authors

  • Quentin Vanderbecq
    Department of Radiology, AP-HP.Sorbonne, Saint Antoine Hospital, 184 Rue du Faubourg Saint-Antoine, 75012, Paris, France. q.vanderbecq@gmail.com.
  • Maxence Gelard
    Université Paris-Saclay, CentraleSupélec, Gif-sur-Yvette, Inria, CVN, France.
  • Jean-Christophe Pesquet
    Centre de Vision Numérique, Université Paris-Saclay, CentraleSupélec, Inria, 91190, Gif-sur-Yvette, France.
  • Mathilde Wagner
    UMR 7371, Université Sorbonne, CNRS, Inserm U114615, rue de l'École de Médecine, 75006, Paris, France.
  • Lionel Arrive
    Department of Radiology, AP-HP.Sorbonne, Saint Antoine Hospital, 184 Rue du Faubourg Saint-Antoine, 75012, Paris, France.
  • Marc Zins
    DRIM France IA, 75013 Paris, France; Department of Medical Imaging, Saint-Joseph Hospital, 75014 Paris, France.
  • Emilie Chouzenoux
    Center for Visual Computing, CentraleSupelec, INRIA Saclay, Gif-sur-Yvette, 91190, France.

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