Machine learning identification of surgical and operative factors associated with surgical expertise in virtual reality simulation

Authors: Alexander Winkler-Schwartz, Recai Yilmaz, Nykan Mirchi, Vincent Bissonnette, Nicole Ledwos, Samaneh Siyar, Hamed Azarnoush, Bekir Karlik, Rolando Del Maestro

Publication date: 2019

Journal: JAMA network open

Publisher: American Medical Association

Description: Despite advances in the assessment of technical skills in surgery, a clear understanding of the composites of technical expertise is lacking. Surgical simulation allows for the quantitation of psychomotor skills, generating data sets that can be analyzed using machine learning algorithms. To identify surgical and operative factors selected by a machine learning algorithm to accurately classify participants by level of expertise in a virtual reality surgical procedure. Fifty participants from a single university were recruited between March 1, 2015, and May 31, 2016, to participate in a case series study at McGill University Neurosurgical Simulation and Artificial Intelligence Learning Centre. Data were collected at a single time point and no follow-up data were collected. Individuals were classified a priori as expert (neurosurgery staff), seniors (neurosurgical fellows and …

Total citations: 117

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Machine learning identification of surgical and operative factors associated with surgical expertise in virtual reality simulation

Machine learning identification of surgical and operative factors associated with surgical expertise in virtual reality simulation

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Machine learning identification of surgical and operative factors associated with surgical expertise in virtual reality simulation