| Autor: |
Siyar, Samaneh, Azarnoush, Hamed, Rashidi, Saeid, Winkler-Schwartz, Alexandre, Bissonnette, Vincent, Ponnudurai, Nirros, Del Maestro, Rolando F. |
| Rok vydání: |
2018 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| DOI: |
10.1007/s11517-020-02155-3 |
| Popis: |
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 |
| Databáze: |
arXiv |
| Externí odkaz: |
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