Deep learning model utilizing clinical data alone outperforms image-based model for hernia recurrence following abdominal wall reconstruction with long-term follow up.

Journal: Surgical endoscopy
PMID: 38862826

Abstract

BACKGROUND: Deep learning models (DLMs) using preoperative computed tomography (CT) imaging have shown promise in predicting outcomes following abdominal wall reconstruction (AWR), including component separation, wound complications, and pulmonary failure. This study aimed to apply these methods in predicting hernia recurrence and to evaluate if incorporating additional clinical data would improve the DLM's predictive ability.

Authors

  • Hadley H Wilson
    Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA.
  • Chiyu Ma
    Department of Statistical Science, Duke University, Durham, NC, USA.
  • Dau Ku
    Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA.
  • Gregory T Scarola
    Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA.
  • 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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