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Computer vision can automatically measure laparoscopic surgical skills using the Fundamentals of Laparoscopic Surgery (FLS) box trainer. This technology differentiates between novice and expert performance, enabling independent skill assessment.

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Area of Science:

  • Surgical Education
  • Medical Simulation
  • Computer Vision Applications

Background:

  • The Fundamentals of Laparoscopic Surgery (FLS) box trainer is a standard for assessing laparoscopic skills, requiring manual observer evaluation.
  • Current performance metrics in the FLS Peg Transfer task include time and penalties for dropped pegs.
  • The need for objective, automated performance measurement in surgical training is critical.

Purpose of the Study:

  • To evaluate the efficacy of computer vision (CV) for automated performance measurement in the FLS box trainer.
  • To develop and validate CV-based metrics for assessing laparoscopic skills.
  • To determine if CV metrics can differentiate surgical skill levels.

Main Methods:

  • Developed a CV system using YOLOv4 deep neural network to analyze videos of the FLS Peg Transfer task.
  • Defined four metric groups: Time, Grasper Movement Speed, Path Efficiency, and Grasper Coordination.
  • Assessed validity by comparing performance across three experience levels: students/interns, residents, and senior surgeons.

Main Results:

  • CV-based metrics significantly differentiated performance based on experience level for most measures.
  • Senior surgeons and residents demonstrated superior performance compared to students and interns.
  • The CV system successfully tracked object transfer and generated detailed feedback reports.

Conclusions:

  • Automated CV metrics provide a more detailed analysis of laparoscopic surgical performance than current methods.
  • These metrics can reliably distinguish between novice and expert skill levels.
  • The CV system enables independent training and assessment without requiring a dedicated observer.