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

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Abstract

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.

Using several scenarios of virtual reality neurosurgical tumor resection together with machine learning classifiers to distinguish skill level

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Acknowledgments

We thank all the neurosurgeons, residents, and medical students from the Montreal Neurological Institute and Hospital and other institutions who participated in this study. We would also like to thank Robert DiRaddo, Group Leader, Simulation, Life Sciences Division, National Research Council Canada at Boucherville and his team, including Denis Laroche, Valérie Pazos, Nusrat Choudhury, and Linda Pecora, for their support in the development of the scenarios used in these studies and all the members of the Simulation, Life Sciences Division, National Research Council Canada.

Funding

This work was supported by the Di Giovanni Foundation, the Montreal English School Board, the B-Strong Foundation, the Colannini Foundation, the Montreal Neurological Institute and Hospital, and the McGill Department of Orthopedics.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Samaneh Siyar & Hamed Azarnoush

  2. Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, 3801 University, E2.89, Montreal, Quebec, H3A 2B4, Canada

    Samaneh Siyar, Hamed Azarnoush, Alexander Winkler-Schwartz, Vincent Bissonnette, Nirros Ponnudurai & Rolando F. Del Maestro

  3. Science and Research Branch, Islamic Azad University, Tehran, Iran

    Saeid Rashidi

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  1. Samaneh Siyar
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Correspondence to Hamed Azarnoush.

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Samaneh Siyar is a Visiting Scholar in the Neurosurgical Simulation Research and Training Centre. Dr. H. Azarnoush previously held the Postdoctoral Neuro-Oncology Fellowship from the Montreal Neurological Institute and Hospital and is a Visiting Professor in the Neurosurgical Simulation Research and Training Centre. Dr. Winkler-Schwartz holds a Robert Maudsley Fellowship from the Royal College of Physicians and Surgeons of Canada and Nirros Ponnudurai is supported by a Heffez Family Bursary. Dr. Del Maestro is the William Feindel Emeritus Professor in Neuro-Oncology at McGill University.

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The authors declare that they have no conflict of interest.

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Research Ethics Board at McGill University approved this study and Informed consent was obtained for experimentation with human subjects.

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Siyar, S., Azarnoush, H., Rashidi, S. et al. Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task. Med Biol Eng Comput 58, 1357–1367 (2020). https://doi.org/10.1007/s11517-020-02155-3

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  • DOI: https://doi.org/10.1007/s11517-020-02155-3

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