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Automation of surgical skill assessment using a three-stage machine learning algorithm.

Joël L Lavanchy1, Joel Zindel1, Kadir Kirtac2

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Summary

This study introduces a machine learning method to automatically assess surgical skills from laparoscopic cholecystectomy videos. The AI model achieved 87% accuracy in distinguishing good from poor surgical performance, aiding surgical training.

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Area of Science:

  • Medical technology
  • Artificial intelligence in surgery
  • Surgical education

Background:

  • Surgical skills directly impact patient outcomes, necessitating continuous training and objective assessment.
  • Current surgical skill assessment methods are manual, time-consuming, and subjective, limiting their effectiveness.
  • Automating surgical skill assessment can provide objective, scalable feedback for improved surgical training.

Purpose of the Study:

  • To develop and validate a machine learning approach for automated surgical skill assessment in laparoscopic cholecystectomy.
  • To utilize video analysis and motion feature extraction for objective skill evaluation.
  • To establish a foundation for AI-driven feedback systems in surgical training.

Main Methods:

  • A three-stage machine learning pipeline was implemented.
  • Stage 1: Convolutional Neural Network (CNN) for surgical instrument identification and localization.
  • Stage 2: Extraction of temporal motion features from instrument data.
  • Stage 3: Linear regression model to predict surgical skill levels based on motion features.

Main Results:

  • The proposed three-stage model achieved an accuracy of 87% (±0.2%) in differentiating between good and poor surgical skills.
  • The system successfully identified and tracked surgical instruments within laparoscopic videos.
  • Extracted motion features correlated significantly with assessed surgical skill levels.

Conclusions:

  • Automated surgical skill assessment using machine learning is feasible and shows high accuracy for binary skill classification.
  • This technology represents a significant advancement towards objective and efficient surgical skill evaluation.
  • Further development is needed to reliably quantify the degree of surgical skill, but the potential for improving surgical training is substantial.