This research introduces a novel reasoning segmentation approach that analyzes operating room workflows through digital twins, overcoming limitations of traditional deep learning models.

• Creates flexible, customizable operating room analysis through decomposition of reasoning tasks
• Leverages digital twins to model OR components and their interactions
• Enables adaptable workflow analysis for various OR scenarios without retraining
• Delivers quantitative insights to improve hospital efficiency and patient care

This breakthrough matters for healthcare facilities seeking to optimize operations, reduce costs, and enhance patient care through data-driven decision making.

Operating Room Workflow Analysis via Reasoning Segmentation over Digital Twins