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

Surgical skill assessment has received growing interest in surgery training and quality control due to its essential role in competency assessment and trainee feedback. However, the current assessment methods rarely provide corresponding feedback guidance while giving ability evaluation. We aim to validate an explainable surgical skill assessment method that automatically evaluates the trainee performance of liposuction surgery and provides visual postoperative and real-time feedback.

In this study, machine learning using a model-agnostic interpretable method based on stroke segmentation was introduced to objectively evaluate surgical skills. We evaluated the method on liposuction surgery datasets that consisted of motion and force data for classification tasks.

Our classifier achieved optimistic accuracy in clinical and imitation liposuction surgery models, ranging from 89 to 94%. With the help of SHapley Additive exPlanations (SHAP), we deeply explore the potential rules of liposuction operation between surgeons with variant experiences and provide real-time feedback based on the ML model to surgeons with undesirable skills.

Our results demonstrate the strong abilities of explainable machine learning methods in objective surgical skill assessment. We believe that the machine learning model based on interpretive methods proposed in this article can improve the evaluation and training of liposuction surgery and provide objective assessment and training guidance for other surgeries.