Sensor-based indicators of performance changes between sessions during robotic surgery training

Abstract

Training of surgeons is essential for safe and effective usage of robotic surgery, yet current assessment tools for learning progression are limited. The objective of this study was to measure changes in trainees’ cognitive and behavioral states as they progressed in a robotic surgeon training curriculum at a medical institution. Seven surgical trainees in urology who had no formal robotic training experience participated in the simulation curriculum. They performed 12 robotic skills exercises with varying levels of difficulty repetitively in separate sessions. EEG (electroencephalogram) activity and eye movements were measured throughout to calculate three metrics: engagement index (indicator of task engagement), pupil diameter (indicator of mental workload) and gaze entropy (indicator of randomness in gaze pattern). Performance scores (completion of task goals) and mental workload ratings (NASA-Task Load Index) were collected after each exercise. Changes in performance scores between training sessions were calculated. Analysis of variance, repeated measures correlation, and machine learning classification were used to diagnose how cognitive and behavioral states associate with performance increases or decreases between sessions. The changes in performance were correlated with changes in engagement index (rrm = −.25, p < .001) and gaze entropy (rrm = −.37, p < .001). Changes in cognitive and behavioral states were able to predict training outcomes with 72.5% accuracy. Findings suggest that cognitive and behavioral metrics correlate with changes in performance between sessions. These measures can complement current feedback tools used by medical educators and learners for skills assessment in robotic surgery training.