Phase-Based Assessment of Arthroscopic Skill Using Motion Smoothness Metrics: A Simulator-based Proof-of-Concept Study

Abstract

Minimally invasive surgeries are meticulous procedures that require complex movement within a limited range of motion, requiring intensive training. Medical training simulators often have limited sensing modalities, restricting the quality of metrics to quantify skill. We created a multi-modal 3D printed simulator that is affordable and easily replicable for remote, automated, and phase-based skill assessment in arthroscopy, a type of minimally invasive surgery. In this pilot study, four subjects of non-medical experience levels performed a peg transfer task in the arthroscopy simulator with synchronized motion and video. The task is segmented into phases to determine the relative efficacy of motion smoothness and other calculated metrics. One phase showed more significant differences in metrics than the other phases, demonstrating the potential for phase-based skill evaluation in tasks with more complex maneuvers. Our novel simulator design allowed for metrics computation at a phase-based level, with initial results demonstrating its importance.