Operative neurosurgery (Hagerstown, Md.) · 2022
Artificial Neural Network Approach to Competency-Based Training Using a Virtual Reality Neurosurgical Simulation.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Reich Aiden, Mirchi Nykan, Yilmaz Recai, Ledwos Nicole, Bissonnette Vincent, Tran Dan Huy, Winkler-Schwartz Alexander, Karlik Bekir, Del Maestro Rolando F
Аннотация
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.
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.