Machine Learning Distinguishes Neurosurgical Skill Levels in a Virtual Reality Tumor Resection Task

PubDate: Nov 2018

Teams: ((1) Neurosurgical Simulation Research and Training Centre, McGill University, Canada, (2) Department of Biomedical Engineering, Amirkabir University of Technology, Iran, (3) Science and Research Branch, Islamic Azad University, Iran)

Writers: Samaneh Siyar (1,2), Hamed Azarnoush (1,2), Saeid Rashidi (3), Alexandre Winkler-Schwartz (1), Vincent Bissonnette (1), Nirros Ponnudurai (1), Rolando F. Del Maestro (1)

PDF: Machine Learning Distinguishes Neurosurgical Skill Levels in a Virtual Reality Tumor Resection Task

Abstract

Background: Virtual reality simulators and machine learning have the potential to augment understanding, assessment and training of psychomotor performance in neurosurgery residents. Objective: This study outlines the first application of machine learning to distinguish “skilled” and “novice” psychomotor performance during a virtual reality neurosurgical task. Methods: Twenty-three neurosurgeons and senior neurosurgery residents comprising the “skilled” group and 92 junior neurosurgery residents and medical students the “novice” group. The task involved removing a series of virtual brain tumors without causing injury to surrounding tissue. Over 100 features were extracted and 68 selected using t-test analysis. These features were provided to 4 classifiers: K-Nearest Neighbors, Parzen Window, Support Vector Machine, and Fuzzy K-Nearest Neighbors. Equal Error Rate was used to assess classifier performance. Results: Ratios of train set size to test set size from 10% to 90% and 5 to 30 features, chosen by the forward feature selection algorithm, were employed. A working point of 50% train to test set size ratio and 15 features resulted in an equal error rates as low as 8.3% using the Fuzzy K-Nearest Neighbors classifier. Conclusion: Machine learning may be one component helping realign the traditional apprenticeship educational paradigm to a more objective model based on proven performance standards.