Unveiling surgical expertise through machine learning in a novel VR/AR spinal simulator: A multilayered approach using transfer learning and connection weights analysis.

Journal: Computers in biology and medicine
PMID: 38944904

Abstract

BACKGROUND: Virtual and augmented reality surgical simulators, integrated with machine learning, are becoming essential for training psychomotor skills, and analyzing surgical performance. Despite the promise of methods like the Connection Weights Algorithm, the small sample sizes (small number of participants (N)) typical of these trials challenge the generalizability and robustness of models. Approaches like data augmentation and transfer learning from models trained on similar surgical tasks address these limitations.

Authors

  • Sami Alkadri
    Musculoskeletal Biomechanics Research Lab, Department of Mechanical Engineering, McGill University, Macdonald Engineering Building, 815 Sherbrooke St W, Montreal, H3A 2K7, QC, Canada; Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, 2200 Leo Pariseau, Suite, 2210, Montreal, H2X 4B3, Quebec, Canada.
  • Rolando F Del Maestro
    Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
  • Mark Driscoll
    Musculoskeletal Biomechanics Research Lab, Department of Mechanical Engineering, McGill University, 845 Sherbrooke St. W, Montréal, Quebec, H3A 0G4, Canada; Orthopaedic Research Laboratory, Research Institute of McGill University Health Centre, Montreal General Hospital, 1650 Cedar Avenue, Montréal, Québec, H3G 1A4, Canada. Electronic address: mark.driscoll@mcgill.ca.

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