Artificial Neural Network Approach to Competency-Based Training Using a Virtual Reality Neurosurgical Simulation.
Oper Neurosurg (Hagerstown)
; 23(1): 31-39, 2022 07 01.
Article
em En
| MEDLINE
| ID: mdl-35726927
ABSTRACT
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Osteófito
/
Realidade Virtual
Limite:
Humans
Idioma:
En
Revista:
Oper Neurosurg (Hagerstown)
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Canadá