Fundamental kinematic indicators predict surgical expertise and clinical outcomes in robot-assisted surgery

Abstract The global proliferation of robotic surgical platforms necessitates a framework for objective skill assessment using fundamental kinematic features that are theoretically extractable from any robotic system with 3D position tracking. Here, we demonstrated that fundamental three-dimensional movement patterns alone can accurately distinguish surgical expertise and show associations with clinical outcomes. A Random Forest classifier discriminated expertise with high case-wise accuracy (area under the curve 0.892 ± 0.075), and feature-importance analysis identified left-hand peak density as the principal discriminator. A small set of these fundamental indicators generalized to unseen surgeons in leave-one-surgeon-out validation, localizing the expertise-associated signal to a few interpretable kinematic measures. Kinematic patterns were correlated with clinical outcomes, and exploratory analysis revealed associations between movement features and blood loss. Individual learning curve analysis revealed heterogeneous adaptation patterns among experts transitioning to the new platform and identified distinct phenotypes with implications for personalized training. These findings show that fundamental kinematic analysis using only three-dimensional positional data can provide interpretable indicators of surgical performance that may extend across robotic systems providing coordinate data.