Objective performance indicators (OPIs) derived from robotic surgery are showing potential for automated skill assessment, but their high dimensionality, data sparsity, and lack of functional context limit their clinical utility and interpretability. Here, we introduce and validate a semantic taxonomy that automatically classifies surgical instruments into functional roles—such as ‘Dominant’, ‘Active Retractor’, and ‘Passive Retractor’—based on their kinematic signatures. Applied to 462 cholecystectomies, hernia repairs, and sleeve gastrectomies, this framework drastically reduced data dimensionality. In predictive modeling for surgical experience and task efficiency, taxonomy-structured OPIs achieved superior performance to conventional metrics while requiring substantially fewer features to reach optimal results (mean, 12.4 vs. 19.5; P = 0.025). By providing functional context, this approach streamlines kinematic analysis, creating a more scalable and interpretable foundation for objective skill assessment, actionable feedback, and data-driven surgical training, ultimately enhancing surgical quality and safety.
Ballo et al. (Fri,) studied this question.