Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task

Authors: Samaneh Siyar, Hamed Azarnoush, Saeid Rashidi, Alexander Winkler-Schwartz, Vincent Bissonnette, Nirros Ponnudurai, Rolando F Del Maestro

Publication date: 2020

Journal: Medical & biological engineering & computing

Publisher: Springer Berlin Heidelberg

Description: 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 …

Total citations: 23

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Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task

Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task

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Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task