Towards automated surgical skill evaluation of endovascular catheterization tasks based on force and motion signatures

Despite the increased use of robotic catheter navigation systems, and the growing interest in surgical skill evaluation in the field of endovascular intervention, there is a lack of objective and quantitative metrics for performance evaluation. So far very little research has studied operator behavioral patterns using catheter kinematics, operator forces and motions, and catheter-tissue interactions. This paper proposes a framework for automated and objective assessment of performance by measuring catheter-tissue contact forces and operator motion patterns across different skill levels, and using language models to learn the underlying force and motion patterns that are characteristic of skill. Discrete HMMs are utilized to model operator behavior for varying skill levels performing different catheterization tasks, resulting in cross-validation classification accuracies of 94% (expert) and 98% (novice) using the force-based skill models, as well as 83% (expert) and 94% (novice) using the motion-based models. The results motivate the design of improved metrics for endovascular skill assessment with further applications towards performance evaluation of robot-assisted endovascular catheterization.