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

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.

Methods

Laparoscopic cholecystectomy videos were obtained from one public and two private data sources. Representative frames were chosen for PGS grading and manually labeled. Three surgical experts independently assigned PGS scores to the selected frames. They then convened as a panel to decide on

SUBMITTER: Olsen GH 

PROVIDER: S-EPMC12500764 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature