# Privacy-Preserving Operating Room Workflow Analysis using Digital Twins

arXiv: https://arxiv.org/abs/2504.12552 · HTML: https://mlsanity.com/paper/2504.12552/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [DETRs with Collaborative Hybrid Assignments Training](https://mlsanity.com/paper/2211.12860/) (2022)
- [Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery](https://mlsanity.com/paper/2006.16166/) (2020)
- [DisguisOR: Holistic Face Anonymization for the Operating Room](https://mlsanity.com/paper/2307.14241/) (2023)
- [MuST: Multi-Scale Transformers for Surgical Phase Recognition](https://mlsanity.com/paper/2407.17361/) (2024)
- [Multi-Modal Unsupervised Pre-Training for Surgical Operating Room Workflow Analysis](https://mlsanity.com/paper/2207.07894/) (2022)

## Built on by (most cited first, 3 of 3)

- [Egosurg: Arbitrary view synthesis for egocentric replay of operating room workflows from ambient cameras](https://mlsanity.com/paper/2510.04802/) (2025)
- [TwinOR: Photorealistic Digital Twins of Dynamic Operating Rooms for Embodied AI Research](https://mlsanity.com/paper/2511.07412/) (2025)
- [A Comprehensive Survey on Surgical Digital Twin](https://mlsanity.com/paper/2512.00019/) (2025)

## Abstract (arXiv)

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.