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Updated: Jun 22, 2025
Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
Published on: April 5, 2024
Unveiling surgical expertise through machine learning in a novel VR/AR spinal simulator: A multilayered approach
Sami Alkadri1, Rolando F Del Maestro2, Mark Driscoll3
1Musculoskeletal 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.
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.
Objective:
To demonstrate the efficacy of artificial neural network and transfer learning algorithms in evaluating virtual surgical performances, applied to a simulated oblique lateral lumbar interbody fusion technique in an augmented and virtual reality simulator.
Design:
The study developed and integrated artificial neural network algorithms within a novel simulator platform, using data from the simulated tasks to generate 276 performance metrics across motion, safety, and efficiency. Innovatively, it applies transfer learning from a pre-trained ANN model developed for a similar spinal simulator, enhancing the training process, and addressing the challenge of small datasets.
Setting:
Musculoskeletal Biomechanics Research Lab; Neurosurgical Simulation and Artificial Intelligence Learning Centre, McGill University, Montreal, Canada.
Participants:
Twenty-seven participants divided into 3 groups: 9 post-residents, 6 senior and 12 junior residents.
Results:
Two models, a stand-alone model trained from scratch and another leveraging transfer learning, were trained on nine selected surgical metrics achieving 75 % and 87.5 % testing accuracy respectively.
Conclusions:
This study presents a novel blueprint for addressing limited datasets in surgical simulations through the strategic use of transfer learning and data augmentation. It also evaluates and reinforces the application of the Connection Weights Algorithm from our previous publication. Together, these methodologies not only enhance the precision of performance classification but also advance the validation of surgical training platforms.
