Artificial Intelligence for Surgical Safety: Automatic Assessment of the Critical View of Safety in Laparoscopic Cholecystectomy Using Deep Learning.

Journal: Annals of surgery
Published Date: May 1, 2022

Abstract

OBJECTIVE: To develop a deep learning model to automatically segment hepatocystic anatomy and assess the criteria defining the critical view of safety (CVS) in laparoscopic cholecystectomy (LC).

Authors

  • Pietro Mascagni
    IHU Strasbourg, Strasbourg, France.
  • Armine Vardazaryan
    ICube, University of Strasbourg, CNRS, IHU Strasbourg, France.
  • Deepak Alapatt
    ICube, University of Strasbourg, CNRS, IHU Strasbourg, France.
  • Takeshi Urade
    IHU Strasbourg - Institut de Chirurgie Guidée par l'image, Strasbourg, France.
  • Taha Emre
    ICube, University of Strasbourg, CNRS, IHU Strasbourg, Strasbourg, France.
  • Claudio Fiorillo
    Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
  • Patrick Pessaux
    Institut Hospitalo-Universitaire de Strasbourg (IHU) Digestive and Endocrine Surgery, Nouvel Hôpital Civil, University of Strasbourg, 1, Place de l'Hôpital, 67091, Strasbourg, France, patrick.pessaux@chru-strasbourg.fr.
  • Didier Mutter
    Institut Hospitalo-Universitaire, Institute of Image-Guided Surgery, University of Strasbourg, Fédération de Médecine Translationnelle de Strasbourg, Strasbourg, France3Department of Digestive Surgery, Strasbourg University Hospital, Fédération de Médecin.
  • Jacques Marescaux
  • Guido Costamagna
    Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
  • Bernard Dallemagne
    IHU Strasbourg - Institut de Chirurgie Guidée par l'image, Strasbourg, France.
  • Nicolas Padoy
    IHU Strasbourg, Strasbourg, France.

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