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Automated Vision-Based Microsurgical Skill Analysis in Neurosurgery Using Deep Learning: Development and Preclinical

Joseph Davids1, Savvas-George Makariou2, Hutan Ashrafian3

  • 1Department of Surgery and Cancer, Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, St. Mary's Praed St., Paddington, London, United Kingdom; Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom.

World Neurosurgery
|February 15, 2021
PubMed
Summary

This study introduces a novel vision-based framework for objectively assessing microsurgical skills in neurosurgery training. The system accurately differentiates skill levels, aiding in technical skill development.

Keywords:
Artificial intelligenceComputer visionConvolutional neural networkMask RCNNMicrosurgeryMotion-analysisNeurosurgery

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Area of Science:

  • Neurosurgery
  • Surgical Skill Assessment
  • Medical Education Technology

Background:

  • Technical skill acquisition is crucial for neurosurgical training.
  • Objective feedback is vital for optimal learning and performance improvement.
  • Current methods for microsurgical skill assessment lack objectivity.

Purpose of the Study:

  • To develop a vision-based framework for automated and objective assessment of microsurgical skill.
  • To utilize a novel representation of surgical tool motion and interactions.
  • To enhance neurosurgical training and technical skill evaluation.

Main Methods:

  • Utilized videos from surgeons of varying skill levels (expert, intermediate, novice) performing arachnoid dissection.
  • Employed a mask region convolutional neural network (CNN) for tool segmentation.
  • Applied novel triangulation metrics for tool motion analysis and skill classification.

Main Results:

  • The framework achieved an area under the curve of 0.977 and 84.21% accuracy in classifying skill levels.
  • Experts demonstrated significantly lower median dissector velocity compared to novices (116.38 ms⁻¹ vs. 190.38 ms⁻¹).
  • Experts maintained a smaller inter-tool tip distance (median 46.78 vs. 75.92) compared to novices.

Conclusions:

  • Automated, objective microsurgery analysis is feasible using CNNs and novel tool motion representations.
  • The developed framework supports technical skills training and assessment in neurosurgery.
  • This technology has the potential to standardize and improve neurosurgical education.