Personalized assessment and training of neurosurgical skills in virtual reality: An interpretable machine learning approach

Abstract

Background: Virtual reality technology has been widely used in surgical simulators, providing new opportunities for assessing and training surgical skills. Machine learning algorithms are commonly used to analyze and evaluate the performance of participants. However, their interpretability limits the personalization of the training for individual participants. Methods: Seventy-nine participants were recruited and divided into three groups based on their skill level in intracranial tumor resection. Data on the use of surgical tools were collected using a surgical simulator. Feature selection was performed using the Minimum Redundancy Maximum Relevance and SVM-RFE algorithms to obtain the final metrics for training the machine learning model. Five machine learning algorithms were trained to predict the skill level, and the support vector machine performed the best, with an accuracy of 92.41% and Area Under Curve value of0.98253. The machine learning model was interpreted using Shapley values to identify the important factors contributing to the skill level of each participant. Results: This study demonstrates the effectiveness of machine learning in differentiating the evaluation and training of virtual reality neurosurgical per- formances. The use of Shapley values enables targeted training by identifying deficiencies in individual skills. Conclusions: This study provides insights into the use of machine learning for personalized training in virtual reality neurosurgery. The interpretability of the machine learning models enables the development of individualized training programs. In addition, this study highlighted the potential of explanatory models in training external skills.

Keywords

• Machine learning
• Neurosurgery
• Shapley values
• Virtual reality
• Human-robot interaction