155 A Machine Learning-Based Approach for Predicting Surgeons’ Subjective Experience and Skill Levels: Neuroimaging Study

Abstract

Aim Measuring cognitive load is important for surgical education and patient safety. Traditional approaches of measuring cognitive load of surgeons utilise behavioural metrics to measure performance and surveys and questionnaires to collect reports of subjective experience. There is a need for more automated, more accurate and objective evaluation methods. Method Functional neuroimaging data was collected using wireless NIRS device from sixteen surgeons (11 attending surgeons and 5 surgery resident) and 17 students while they performed two laparoscopic tasks (Peg transfer and String pass). Participant’s subjective mental load was assessed using NASA-TLX survey. Machine learning approaches were used for predicting the subjective experience and skill levels. Results The Prefrontal cortex (PFC) activations were greater in students who reported higher-than-median task load, as measured by the NASA-TLX survey. However, in the case of attending surgeons the opposite tendency was observed, namely higher activations in lower v higher task loaded subjects. We found response was greater in the left PFC of students particularly near dorso- and ventrolateral areas. We quantified the ability of PFC activation to predict differences in skill and task load using machine learning while focusing on the effects of NIRS channel separation distance on the results. Our results showed that the classification of skill level and subjective task load could be predicted based on PFC activation with an accuracy of nearly 90%. Conclusions The high accuracy of results is encouraging and suggest the integration of the strategy developed in this study as a promising approach to design automated, more accurate and objective evaluation methods.