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Artificial Neural Networks to Assess Virtual Reality Anterior Cervical Discectomy Performance.

Nykan Mirchi1, Vincent Bissonnette1,2, Nicole Ledwos1

  • 1Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.

Operative Neurosurgery (Hagerstown, Md.)
|December 14, 2019
PubMed
Summary

Artificial intelligence and virtual reality surgical simulators identify key performance metrics for surgical training. Safety metrics, like minimizing spinal dura contact, are crucial for distinguishing expertise levels in simulated procedures.

Keywords:
Anterior cervical discectomyArtificial intelligenceArtificial neural networksMachine learningSimulationSurgical trainingVirtual reality

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Area of Science:

  • Surgical Simulation
  • Artificial Intelligence
  • Medical Education

Background:

  • Virtual reality (VR) surgical simulators offer safe, self-guided training environments.
  • Artificial intelligence (AI), specifically artificial neural networks (ANNs), can analyze large datasets from simulators.
  • AI can provide insights into critical performance metrics during simulated surgical tasks.

Purpose of the Study:

  • To differentiate performance in a VR-simulated anterior cervical discectomy (ACD).
  • To identify novel performance metrics for ACD.
  • To determine the relative importance of these metrics using ANNs.

Main Methods:

  • Twenty-one participants (junior, senior, post-resident) performed a simulated ACD on the Sim-Ortho VR simulator.
  • Performance data were collected and analyzed to calculate numerous metrics.
  • ANNs were trained and tested to assess metric importance.

Main Results:

  • 369 metrics across safety, efficiency, motion, and cognition were generated.
  • An ANN achieved 100% training accuracy and 83.3% testing accuracy.
  • Safety metrics, such as spinal dura contacts, were identified as highly important.

Conclusions:

  • ANNs successfully classified participant expertise levels.
  • This methodology reveals the key metrics contributing to surgical expertise.
  • Understanding these metrics enhances surgical training and performance evaluation.