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

作　　者：Fei LI Zhibao QIN Kai QIAN Shaojun LIANG Chengli LI Yonghang TAI 

机构地区：[1]Yunnan Key Laboratory of Opto-electronic Information Technology,Yunnan Normal University,Kunming 650500,China [2]Department of Thoracic Surgery,Institute of The First People’s Hospital of Yunnan Province,Kunming 650500,China [3]Faculty of Life and Biotechnology,Kunming University of Science and Technology,Kunming 650031,China

出　　处：《虚拟现实与智能硬件（中英文）》2024年第1期17-29,共13页Virtual Reality & Intelligent Hardware

基　　金：Supported by the Yunnan Key Laboratory of Opto-Electronic Information Technology,Postgraduate Research Innovation Fund of Yunnan Normal University (YJSJJ22-B79);the National Natural Science Foundation of China (62062069,62062070,62005235)。

摘　　要：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 of 0.98253.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 performances.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.

关 键 词：Machine learning NEUROSURGERY Shapley values Virtual reality Human-robot interaction 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术] R651[医药卫生—外科学]