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Simulator Training for Endovascular Neurosurgery
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Computer Assisted Objective Assessment of Micro-Neurosurgical Skills From Intraoperative Videos.

Pon Deepika1, Kalahasti V V Deepesh1, Phani Sriram Vadali1

  • 1IIIT-Bangalore Bangalore 560100 India.

IEEE Open Journal of Engineering in Medicine and Biology
|April 14, 2023
PubMed
Summary

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Intraoperative ultrasonography with intraoperative MRI confirmation in glioma surgery: Evaluating a combined resection strategy.

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Prognostic Utility of Noninvasive Brain Monitoring in Moderate-to-Severe Cerebral Venous Thrombosis: A Prospective Observational Study.

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This study introduces an automated framework using AI to objectively assess microsurgical skills by analyzing instrument movements in videos. Experts show distinct patterns like smoother motions and more frequent microscope adjustments compared to novices.

Area of Science:

  • Neurosurgery
  • Computer Vision
  • Surgical Skill Assessment

Background:

  • Traditional surgical skill assessment relies on subjective observation and patient outcomes, which are time-consuming and effort-intensive.
  • Objective and automated methods are needed to evaluate surgical proficiency efficiently.

Purpose of the Study:

  • To develop a framework for the automated, objective assessment of microsurgical skill.
  • To differentiate between novice and expert neurosurgeons using quantifiable metrics.

Main Methods:

  • A Mask Region-based Convolution Neural Network (RCNN) was employed to detect and track surgical instruments in neurosurgery videos.
  • Tool motion metrics (velocity, acceleration, jerk, path length) and handling metrics (bimanual handling, idle time, inter-tool-tip distance) were computed.
Keywords:
Computer visionYolov5mask RCNNmicro-neurosurgerymotion analysisskill assessment

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  • Microscope adjustment patterns were analyzed using time-based metrics.

    • Main Results:

    • Experts demonstrated significantly lower velocity, acceleration, and jerk compared to novices.
    • Novice surgeons exhibited longer idle times and greater inter-tool-tip distances.
    • Experts utilized bimanual handling more frequently and made more frequent, shorter microscope adjustments.

    Conclusions:

    • The developed vision-based framework provides a reliable method for objective surgical skill assessment.
    • The system offers prompt and precise feedback to neurosurgeons for skill improvement.
    • Automated analysis of surgical video can effectively quantify skill levels.