Using artificial intelligence to model expert panel diagnosis of cholecystitis severity.

Journal: Surgical endoscopy
Published Date: Aug 18, 2025

Abstract

BACKGROUND: Determining cholecystitis severity via the clinically validated Parkland Grading Scale (PGS) is useful for predicting case difficulty and likelihood of postoperative complications. A panel assessment by multiple surgeons can reduce variation in PGS due to subjectivity, but is time-consuming. An artificial intelligence (AI) model trained on the assessments of an expert clinician panel may improve efficiency and reduce variability in diagnosis in image-based assessments.

Authors

  • Griffin H Olsen
    Intermountain Healthcare Delivery Institute, Intermountain Health, Salt Lake City, UT, USA.
  • Emmett D Goodman
    Department of Computer Science, Stanford University, Stanford, California.
  • Josiah G Aklilu
    Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Sebastiano Bartoletti
    Department of Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Room H3591, Stanford, CA, 94305-5641, USA.
  • Kay S Hung
    Department of Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Room H3591, Stanford, CA, 94305-5641, USA. kayhung@stanford.edu.
  • Janice H Yang
    Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Eric C Sorenson
    Department of Surgery, Intermountain Medical Center, Murray, UT, USA.
  • Jeffrey K Jopling
  • Serena Y Yeung
    Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Dan E Azagury

Keywords

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