For using robotic surgery effectively and safely, training of surgeons is of paramount importance. However, the current tools for assessing learning progression are limited. A study was conducted for evaluating changes in trainees cognitive and behavioral states as they progressed in a robotic surgeon training curriculum at a medical institution. In this study, 7 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 different sessions. Electroencephalogram activity and eye movements were measured throughout to calculate 3 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 every exercise and changes in performance scores between training sessions were calculated. Analysis of variance, repeated measures correlation, and machine learning classification were used to diagnose the link between cognitive and behavioral states and increase or decrease in performance between sessions. It was reported that changes in performance 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. These findings indicate an association between cognitive and behavioral metrics and changes in performance between sessions. These measures can complement current feedback tools used by medical educators and learners for evaluating skills in robotic surgery training.

Source: Appl Ergon. 2021 Jan;90:103251.