Toward precision surgical education: quantitative evaluation of surgical performance using instantaneous screw axes

In surgical skill development, a trainee's goal is to move through the learning curve and achieve expert performance. Goal directed training, with expert performance as the goal, can facilitate skill development; however, there are currently few methods available to encode expert-like simulation performance into learning strategies that can be practiced independently.

We propose a novel method of surgical simulation skill analysis through segmenting and evaluating kinematic data with instantaneous screw axes (ISA) theory and K-means clustering. In ISA, single degree of freedom (DOF) tasks can be represented as displacements about a single screw axis; however, we propose extending this method to more complex tasks defining them with clusters of similar ISAs in the surgical environment, decomposing them into a sequence of 1DOF movements. In this paper, we present an ISA algorithm and apply it to surgeon manipulator poses across fourteen suturing and knot-tying gestures obtained from the JIGSAWS surgical dataset. We also apply this method to entire simulated suturing demonstrations across a 6-month training period from the BGU-SKILLS dataset. We implemented K-means clustering to segment these movements into sub-gestures. We hypothesize that individuals with greater levels of expertise should exhibit more concise actions with minimal extraneous movement; therefore, fewer clusters should be required to decompose their simulation performance.

Our ISA algorithm was applied to 1136 gestures from ten surgeons across three skill levels and 324 unsegmented demonstrations collected from 18 surgical residents over a training period of 6 months. We performed a Kruskal-Wallis analysis with a Dunn-Sidak post-hoc test on the number of ISA clusters required to decompose each gesture. We found that highly task-constrained gestures required significantly fewer numbers of clusters for expert and/or intermediate groups when compared to novices on suturing tasks only.

Our results suggest that this method can be used to identify task-constrained gestures within independently performed suturing surgical simulations and classify them into higher skill and lower skill sets. This analysis can also provide geometric feedback on performed gestures vs expert gestures, providing personalized automated performance analysis for surgical trainees leading to personalized educational training.