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An explainable machine learning method for assessing surgical skill in liposuction surgery.

Sutuke Yibulayimu1, Yuneng Wang2, Yanzhen Liu1

  • 1School of Biomedical Science and Medical Engineering, Beihang University, Beijing, China.

International Journal of Computer Assisted Radiology and Surgery
|September 28, 2022
PubMed
Summary

This study introduces an explainable machine learning method for surgical skill assessment in liposuction. The model accurately evaluates performance and provides real-time feedback to improve surgical training.

Keywords:
Interpretable machine learningLiposuction surgeryObjective skill assessmentSurgical educationSurgical motion

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Area of Science:

  • Surgical Education
  • Machine Learning in Medicine
  • Medical Simulation

Background:

  • Surgical skill assessment is crucial for training and quality control.
  • Current methods often lack direct feedback guidance.
  • Objective evaluation and real-time feedback are needed for surgical trainees.

Purpose of the Study:

  • To validate an explainable machine learning (ML) method for automated surgical skill assessment.
  • To provide visual postoperative and real-time feedback for liposuction surgery trainees.
  • To enhance competency assessment and trainee feedback in surgical training.

Main Methods:

  • Utilized a model-agnostic interpretable ML method based on stroke segmentation.
  • Evaluated the method on liposuction surgery datasets including motion and force data.
  • Employed SHapley Additive exPlanations (SHAP) for rule exploration and feedback generation.

Main Results:

  • Achieved high accuracy (89-94%) in classifying clinical and imitation liposuction surgery models.
  • Identified operational rules differentiating surgeons with varying experience levels.
  • Provided real-time, ML-based feedback to surgeons with suboptimal skills.

Conclusions:

  • Explainable ML methods show strong potential for objective surgical skill assessment.
  • The proposed ML model can enhance liposuction surgery evaluation and training.
  • This approach offers objective assessment and training guidance applicable to other surgical procedures.