School of Biomedical Science and Medical Engineering, Beihang University, Beijing, China.
Center of Body Contouring and Liposuction Center, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Int J Comput Assist Radiol Surg. 2022 Dec;17(12):2325-2336. doi: 10.1007/s11548-022-02739-4. Epub 2022 Sep 27.
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.
由于在能力评估和学员反馈方面的重要作用，手术技能评估在外科培训和质量控制中受到越来越多的关注。然而，目前的评估方法在进行能力评估的同时很少提供相应的反馈指导。我们旨在验证一种可解释的手术技能评估方法，该方法能够自动评估吸脂手术学员的表现，并提供术后和实时的可视化反馈。
本研究引入了一种基于笔触分割的模型不可知可解释方法的机器学习，以客观评估手术技能。我们在吸脂手术数据集上评估了该方法，这些数据集包含运动和力数据，用于分类任务。
我们的分类器在临床和模拟吸脂手术模型中取得了乐观的准确率，范围从 89%到 94%。借助 SHapley Additive exPlanations (SHAP)，我们深入探讨了具有不同经验的外科医生之间吸脂手术操作的潜在规律，并根据 ML 模型为技能不佳的外科医生提供实时反馈。
我们的结果表明可解释机器学习方法在客观手术技能评估方面具有强大的能力。我们相信，本文提出的基于解释性方法的机器学习模型可以改进吸脂手术的评估和培训，并为其他手术提供客观的评估和培训指导。
