Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Room
This work addresses the need for improved surgical workflow analysis and team coordination by enabling long-term tracking of individuals, though it is incremental as it builds on existing re-identification methods.
The paper tackles the problem of tracking surgical personnel across multiple procedures by developing a generalizable re-identification framework that captures individual movement patterns and physical characteristics, achieving 86.19% accuracy on clinical data and a 12% improvement in transferability between environments.
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.