Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Room

2025-03-17

AI Summary

Existing surgical role modeling approaches neglect individual characteristics—such as kinematic patterns and anthropometric traits—as well as team familiarity, and lack robustness for longitudinal personnel tracking across clinical sites. Method: We propose a generalizable, transferable re-identification framework for surgical staff, the first to jointly model 3D point-cloud-based static morphology and dynamic joint motion—enabling markerless, cross-operating-room long-term tracking. Our method integrates temporal pose representation learning, cross-domain adaptive feature alignment, and multi-view consistency constraints, transcending conventional role-level abstraction. Results: Evaluated on real-world clinical data, our framework achieves 86.19% identification accuracy and 75.27% cross-environment transfer accuracy—surpassing state-of-the-art by 12%—while improving tracking precision by over 50%. It enables fine-grained, spatiotemporal visualization of surgical team dynamics and operating room utilization.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Motion & Tracking
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Surgical domain models improve workflow optimization through automated predictions of each staff member's surgical role. However, mounting evidence indicates that team familiarity and individuality impact surgical outcomes. We present a novel staff-centric modeling approach that characterizes individual team members through their distinctive movement patterns and physical characteristics, enabling long-term tracking and analysis of surgical personnel across multiple procedures. To address the challenge of inter-clinic variability, we develop a generalizable re-identification framework that encodes sequences of 3D point clouds to capture shape and articulated motion patterns unique to each individual. Our method achieves 86.19% accuracy on realistic clinical data while maintaining 75.27% accuracy when transferring between different environments - a 12% improvement over existing methods. When used to augment markerless personnel tracking, our approach improves accuracy by over 50%. Through extensive validation across three datasets and the introduction of a novel workflow visualization technique, we demonstrate how our framework can reveal novel insights into surgical team dynamics and space utilization patterns, advancing methods to analyze surgical workflows and team coordination.

Problem

• Improves surgical workflow optimization through individual staff modeling.
• Addresses inter-clinic variability with generalizable re-identification framework.
• Enhances markerless personnel tracking accuracy by over 50%.

Innovation

• Staff-centric modeling using movement and physical characteristics
• Generalizable re-identification with 3D point cloud encoding
• Improved accuracy in markerless personnel tracking