4.3 Article

Artificial Neural Network Approach to Competency-Based Training Using a Virtual Reality Neurosurgical Simulation

期刊

OPERATIVE NEUROSURGERY
卷 23, 期 1, 页码 31-39

出版社

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1227/ons.0000000000000173

关键词

Anterior cervical diskectomy and fusion; Artificial neural network; Performance metrics; Surgical expertise; Surgical training; Virtual reality simulation

资金

  1. AO Foundation (Davos, Switzerland)
  2. Franco Di Giovani Foundation

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This study outlines the educational utility of using an artificial neural network (ANN) in the assessment and quantitation of surgical expertise, and reveals learning patterns of technical skills through a simulated surgical procedure.
BACKGROUND: The methodology of assessment and training of surgical skills is evolving to deal with the emergence of competency-based training. Artificial neural networks (ANNs), a branch of artificial intelligence, can use newly generated metrics not only for assessment performance but also to quantitate individual metric importance and provide new insights into surgical expertise. OBJECTIVE: To outline the educational utility of using an ANN in the assessment and quantitation of surgical expertise. A virtual reality vertebral osteophyte removal during a simulated surgical spine procedure is used as a model to outline this methodology. METHODS: Twenty-one participants performed a simulated anterior cervical diskectomy and fusion on the Sim-Ortho virtual reality simulator. Participants were divided into 3 groups, including 9 postresidents, 5 senior residents, and 7 junior residents. Data were retrieved from the osteophyte removal component of the scenario, which involved using a simulated burr. The data were manipulated to initially generate 83 performance metrics spanning 3 categories (safety, efficiency, and motion) of which only the most relevant metrics were used to train and test the ANN. RESULTS: The ANN model was trained on 6 safety metrics to a testing accuracy of 83.3%. The contributions of these performance metrics to expertise were revealed through connection weight products and outlined 2 identifiable learning patterns of technical skills. CONCLUSION: This study outlines the potential utility of ANNs which allows a deeper understanding of the composites of surgical expertise and may contribute to the paradigm shift toward competency-based surgical training.

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