  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.
  Keywords: Artificial intelligence, Classifiers, Machine learning,
Neurosurgery skill assessment, Surgical education, Tumor resection, Virtual
reality simulation
