Quantitative Movement Analysis in Endovascular Surgical Tasks for Objective Determination of Skill
Estrada, Sean J
O'Malley, Marcia K.
Doctor of Philosophy
Endovascular surgeons use catheters placed under the skin to access vascular structures, via minimally invasive techniques, in order to treat disease. Endovascular surgery is one of the most rapidly expanding specialties, and the spectrum of cases, from basic to complex, demands a wide range of skills from operators. A key surgical competency requirement is to optimally visualize and utilize pre-shaped catheters and direct vessel interactions, yet current performance assessment techniques are limited to grading scales based solely on subjective ratings. Since most endovascular procedures involve performing fine motor control tasks that require complex, dexterous movements, this thesis explores the potential for a standardized, objective, and quantitative means of measuring technical competence based on analysis of the kinematics of endovascular tool tip motions. To accomplish this goal, an experiment was designed that involved recording the catheter tip movement from twenty-one subjects performing four fundamental endovascular tasks in each of three sessions. Participants performed both manual and robotic methods of catheterization in an inanimate model and performed manual catheterization in a simulation environment with a virtual representation of the same model. Electromagnetic sensors were used to capture catheter tip movement when performing tasks on the physical model, while image processing techniques were used to track catheter tip movements from the fluoroscopic images when performing tasks on the simulator. Several motion-based performance measures that have been shown to reliably assess skill in other domains were computed and tested for correlation with subjective data that were simultaneously obtained from the global rating scale assessment tool. The selected set of motion-based metrics captured both the kinematics of tool tip motion (path length, tip accelerations) and the quality of movement (smoothness). The metrics that captured movement quality produced reliable correlations with the observation-based assessment metrics. Further, these metrics were able to differentiate skill among participants. These objective and quantitative metrics that capture movement quality could be incorporated into future training protocols to provide detailed feedback on trainee performance.
Skill assessment; Motion capture; Virtual reality; Surgical robotic devices