Privacy-Preserving Operating Room Workflow Analysis using Digital Twins

• URL: http://arxiv.org/abs/2504.12552v2
• Date: Thu, 03 Jul 2025 06:11:55 GMT
• Title: Privacy-Preserving Operating Room Workflow Analysis using Digital Twins
• Authors: Alejandra Perez, Han Zhang, Yu-Chun Ku, Lalithkumar Seenivasan, Roger Soberanis, Jose L. Porras, Richard Day, Jeff Jopling, Peter Najjar, Mathias Unberath,
• Abstract summary: We propose a two-stage pipeline for privacy-preserving operating room (OR) video analysis and event detection.<n>First, we leverage vision foundation models for depth estimation and semantic segmentation to generate Digital Twins of the OR from conventional RGB videos.<n>Second, we employ the SafeOR model, a fused two-stream approach that processes segmentation masks and depth maps for OR event detection.
• Score: 38.744671293771695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The operating room (OR) is a complex environment where optimizing workflows is critical to reduce costs and improve patient outcomes. While computer vision approaches for automatic recognition of perioperative events can identify bottlenecks for OR optimization, privacy concerns limit the use of OR videos for automated event detection. We propose a two-stage pipeline for privacy-preserving OR video analysis and event detection. First, we leverage vision foundation models for depth estimation and semantic segmentation to generate de-identified Digital Twins (DT) of the OR from conventional RGB videos. Second, we employ the SafeOR model, a fused two-stream approach that processes segmentation masks and depth maps for OR event detection. Evaluation on an internal dataset of 38 simulated surgical trials with five event classes shows that our DT-based approach achieves performance on par with -- and sometimes better than -- raw RGB video-based models for OR event detection. Digital Twins enable privacy-preserving OR workflow analysis, facilitating the sharing of de-identified data across institutions and potentially enhancing model generalizability by mitigating domain-specific appearance differences.

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