用数字孪生技术分析手术室流程，保护隐私还能提升模型效果。
Privacy-Preserving Operating Room Workflow Analysis using Digital Twins
- 从普通视频生成去标识化的数字孪生，不暴露真实画面。
- 在38次模拟手术中，识别准确率与原始视频相当甚至更好。
- 适合医疗数据共享、跨机构研究的隐私保护场景。
手术室是复杂环境，优化流程可降低成本并改善患者结局。尽管计算机视觉能自动识别围术期事件以发现瓶颈，但隐私顾虑限制了手术视频用于自动化事件检测。本文提出一种两阶段隐私保护手术室视频分析与事件检测方法：首先利用视觉基础模型进行深度估计和语义分割，从常规RGB视频生成去标识化的数字孪生（DT）；其次采用SafeOR模型，融合双流结构处理分割掩码与深度图实现手术事件检测。在包含38个模拟手术试验、5类事件的内部数据集上评估表明，基于数字孪生的方法在事件检测性能上与基于原始RGB视频的模型相当，甚至有时更优。数字孪生实现了隐私保护下的手术室流程分析，促进机构间去标识数据共享，并可能通过减少特定场景外观差异提升模型泛化能力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。