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Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual

Alexander Winkler-Schwartz1, Recai Yilmaz1, Nykan Mirchi1

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Summary

Machine learning algorithms can accurately classify surgical expertise using virtual reality simulation data. This technology shows promise for objectively assessing surgeon skill levels with high precision.

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Area of Science:

  • Surgical Education and Training
  • Artificial Intelligence in Medicine
  • Neurosurgery Simulation

Background:

  • Assessing surgical technical expertise remains challenging despite advancements.
  • Surgical simulation generates quantifiable psychomotor skill data.
  • Machine learning (ML) offers potential for analyzing complex surgical datasets.

Purpose of the Study:

  • To identify key surgical and operative factors for classifying expertise levels.
  • To utilize ML algorithms for accurate participant classification in a virtual reality (VR) surgical procedure.

Main Methods:

  • Fifty participants (medical students to neurosurgeons) performed simulated VR tumor resections.
  • Performance metrics (instrument movement, force, tissue resection, bleeding) were analyzed.
  • Four ML algorithms (K-nearest neighbor, naive Bayes, discriminant analysis, support vector machine) were employed for classification.

Main Results:

  • The K-nearest neighbor algorithm achieved 90% accuracy in classifying participants by expertise level.
  • ML algorithms identified specific performance metrics crucial for differentiating skill levels.
  • The most accurate algorithm used 6 key performance metrics.

Conclusions:

  • ML algorithms can effectively classify surgical expertise in VR simulations with high accuracy.
  • This approach offers a precise and granular method for assessing surgical skills.
  • VR simulation combined with ML holds significant potential for surgical training and evaluation.