This study outlines the first investigation of application of machine learning to distinguish "skilled" and "novice" psychomotor performance during a virtual reality (VR) brain tumor resection task. Tumor resection task participants included 23 neurosurgeons and senior neurosurgery residents as the "skilled" group and 92 junior neurosurgery residents and medical students as the "novice" group. The task involved removing a series of virtual brain tumors without causing injury to surrounding tissue. Originally, 150 features were extracted followed by statistical and forward feature selection. The selected features were provided to 4 classifiers, namely, K-Nearest Neighbors, Parzen Window, Support Vector Machine, and Fuzzy K-Nearest Neighbors. Sets of 5 to 30 selected features were provided to the classifiers. A working point of 15 premium features resulted in accuracy values as high as 90% using the Supprt Vector Machine. The obtained results highlight the potentials of machine learning, applied to VR simulation data, to help realign the traditional apprenticeship educational paradigm to a more objective model, based on proven performance standards. Graphical abstract Using several scenarios of virtual reality neurosurgical tumor resection together with machine learning classifiers to distinguish skill level.

中文翻译：

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机器学习可区分虚拟现实肿瘤切除任务中的神经外科技能水平。

这项研究概述了机器学习在虚拟现实（VR）脑肿瘤切除任务中区分“熟练”和“新手”精神运动表现的应用的首次调查。肿瘤切除任务的参与者包括“技能”组的23位神经外科医师和高级神经外科医师，以及“新手”组的92位初级神经外科医师和医学生。该任务涉及在不造成周围组织损伤的情况下清除一系列虚拟脑瘤。最初，提取了150个特征，然后进行统计和正向特征选择。所选特征被提供给4个分类器，即K最近邻，Parzen窗口，支持向量机和模糊K最近邻。将5到30个选定特征的集合提供给分类器。使用Supprt向量机，具有15个高级功能的工作点可导致高达90％的精度值。获得的结果凸显了将机器学习应用于VR模拟数据的潜力，以基于行之有效的性能标准，帮助将传统的学徒教育范式重新调整为更客观的模型。图形摘要使用多种虚拟现实场景进行神经外科肿瘤切除，并结合机器学习分类器来区分技能水平。