Measuring provider-level differences in perioperative workflow using computer vision-based artificial intelligence.

ABSTRACT:

Objectives

To evaluate provider-level variability across the full perioperative workflow using a computer vision-based artificial intelligence (AI) system that automatically detects and timestamps operating room events.

Methods

A cross-sectional study of total knee arthroplasty cases performed between September 2022 and March 2025 at a regional health system was conducted. An ambient surgical platform equipped with wall-mounted cameras continuously captured perioperative activity. A YOLO-based model identified patients, staff and equipment, and a transformer-based event detector predicted key perioperative events in real time. Detected events were used to segment cases into eight workflow phases: anaesthesia induction, patient preparation, final preparation, active procedure

SUBMITTER: Loo T 

PROVIDER: S-EPMC12718557 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature