Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Room
Journal:
arXiv
Published Date:
Mar 17, 2025
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.
