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

• URL: http://arxiv.org/abs/2503.13028v2
• Date: Thu, 20 Mar 2025 12:08:07 GMT
• Title: Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Room
• Authors: Tony Danjun Wang, Lennart Bastian, Tobias Czempiel, Christian Heiliger, Nassir Navab,
• Abstract summary: We present a staff-centric modeling approach that characterizes individual team members through their distinctive movement patterns and physical characteristics.<n>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.<n>Our method achieves 86.19% accuracy on realistic clinical data while maintaining 75.27% accuracy when transferring between different environments.
• Score: 36.67627283629717
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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