Imaging Based Surgical Site Infection Detection Using Artificial Intelligence.

Journal: Annals of surgery
Published Date: Jul 3, 2025

Abstract

OBJECTIVE: To develop an AI-based pipeline to assess and triage patient-submitted postoperative wound images.

Authors

  • Hala Muaddi
    Division of General Surgery, Department of Surgery, University of Toronto, Toronto, Canada.
  • Ashok Choudhary
    Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
  • Frank Lee
    Division of Hepatobiliary and Pancreas Surgery, Mayo Clinic, Rochester, MN, USA.
  • Stephanie Anderson
    Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
  • Elizabeth Habermann
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • David Etzioni
    Division of Colon and Rectal Surgery, Mayo Clinic, Phoenix, AZ, USA.
  • Sarah McLaughlin
    Division of Surgical Oncology, Mayo Clinic, Jacksonville, FL, USA.
  • Michael Kendrick
    Division of Hepatobiliary and Pancreas Surgery, Mayo Clinic, Rochester, MN, USA.
  • Hojjat Salehinejad
  • Cornelius Thiels
    Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic Rochester, MN; Department of Surgery, Mayo Clinic Rochester, MN. Electronic address: thiels.cornelius@mayo.edu.

Keywords

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