Development and Validation of Image-Based Deep Learning Models to Predict Surgical Complexity and Complications in Abdominal Wall Reconstruction.

Journal: JAMA surgery
Published Date: Oct 1, 2021

Abstract

IMPORTANCE: Image-based deep learning models (DLMs) have been used in other disciplines, but this method has yet to be used to predict surgical outcomes.

Authors

  • Sharbel Adib Elhage
    Department of Surgery, Franciscus Gasthuis en Vlietland, Rotterdam, the Netherlands.
  • Eva Barbara Deerenberg
    Department of Surgery, Franciscus Gasthuis en Vlietland, Rotterdam, the Netherlands.
  • Sullivan Armando Ayuso
    Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina.
  • Keith Joseph Murphy
    Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina.
  • Jenny Meng Shao
    Department of Surgery, University of Pennsylvania, Philadelphia.
  • Kent Williams Kercher
    Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina.
  • Neil James Smart
    Department of Colorectal Surgery, Royal Devon and Exeter NHS Foundation Trust, Royal Devon and Exeter Hospital, Exeter, United Kingdom.
  • John Patrick Fischer
    Division of Plastic Surgery, Department of Surgery, Perelman School of Medicine, Philadelphia, Pennsylvania.
  • Vedra Abdomerovic Augenstein
    Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina.
  • Paul Dominick Colavita
    Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina.
  • B Todd Heniford
    Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina.

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