Assessing the value of deep neural networks for postoperative complication prediction in pancreaticoduodenectomy patients.

Journal: PloS one
PMID: 39774499

Abstract

INTRODUCTION: Pancreaticoduodenectomy (PD) for patients with pancreatic ductal adenocarcinoma (PDAC) is associated with a high risk of postoperative complications (PoCs) and risk prediction of these is therefore critical for optimal treatment planning. We hypothesize that novel deep learning network approaches through transfer learning may be superior to legacy approaches for PoC risk prediction in the PDAC surgical setting.

Authors

  • Mikkel Bonde
    Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
  • Alexander Bonde
    Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
  • Haytham Kaafarani
    - Harvard Medical School, Trauma, Emergency Surgery and Surgical Critical Care, Massachusetts General Hospital - Boston - MA - Estados Unidos.
  • Andreas Millarch
    Dep. of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
  • Martin Sillesen
    Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.

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