Predicting rare outcomes in abdominal wall reconstruction using image-based deep learning models.

Journal: Surgery
Published Date: Oct 11, 2022

Abstract

BACKGROUND: Deep learning models with imbalanced data sets are a challenge in the fields of artificial intelligence and surgery. The aim of this study was to develop and compare deep learning models that predict rare but devastating postoperative complications after abdominal wall reconstruction.

Authors

  • Sullivan A Ayuso
    Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA.
  • Sharbel A Elhage
    Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC.
  • Yizi Zhang
    Graduate School, Department of Statistics, Columbia University, New York, NY.
  • Bola G Aladegbami
    Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA.
  • Keith S Gersin
    Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC.
  • John P Fischer
    Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Vedra A Augenstein
    Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA. vedra.augenstein@gmail.com.
  • Paul D Colavita
    Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC.
  • B Todd Heniford
    Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina.

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