Abstract
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. These have disadvantages such as sporadic data, occasionally intrusive methodologies, subjective or misleading self-reporting. In addition, traditional approaches use subjective metrics that cannot distinguish between skill levels. Functional neuroimaging data was collected using a high density, 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 the NASA-TLX survey. Machine learning approaches were used for predicting the subjective experience and skill levels. 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 the lower v higher task loaded subjects. We found that response was greater in the left PFC of students particularly near the dorso- and ventrolateral areas. We quantified the ability of PFC activation to predict the differences in skill and task load using machine learning while focussing 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%. Our finding shows that there is sufficient information available in the optical signals to make accurate predictions about the surgeons' subjective experiences and skill levels. 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.
Highlights
Excessive workload or acute stress may impact surgeons’ ability to process all the information available during surgery in the operation room (OR) and may result in low situational and safety awareness, impaired decision-making and performance
We quantified the ability of Prefrontal cortex (PFC) activation to predict the differences in skill and task load using machine learning while focussing 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%
Summary
Excessive workload or acute stress may impact surgeons’ ability to process all the information available during surgery in the operation room (OR) and may result in low situational and safety awareness, impaired decision-making and performance. Several methods have been used to measure workload in surgery, including, subjective rating scales and physiological measurements (EEG, EKG etc) [8]. One of the most widely used is the subjective ratings scales known as NASA-TLX [9,10,11]. It is a multidimensional scale, initially developed for the use in the aviation industry. NASA TLX can be intrusive to primary task performance there is a need for more automated, more accurate and objective evaluation methods
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