Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks
Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks
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DOI：
10.1007/s11548-019-02039-4
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发表时间：
2019-09-01
影响因子：
3
通讯作者：
Muller, Pierre-Alain
中科院分区：
文献类型：
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作者：
Fawaz, Hassan Ismail;Forestier, Germain;Muller, Pierre-Alain
目的观察经验不足的受训者的资深外科医生的手动反馈是一项非常费力的任务，非常昂贵，耗时且易于主观。随着外科手术数量的逐年增加，对受训者的外科技能提供准确、客观和自动的评估以改善外科实践的需求前所未有。方法在本文中，我们设计了一个卷积神经网络（CNN），通过提取机器人手术过程中受训者的动作中的潜在模式来对手术技能进行分类。该方法在JIGSAWS数据集上进行了验证，用于两个手术技能评估任务：分类和回归。我们的研究结果表明，深度神经网络构成了强大的机器学习模型，能够在JIGSAWS数据集上达到新的竞争性最先进的性能。虽然我们利用了CNN的效率，但我们能够使用类激活图技术将其黑盒效应降至最低。结论：这一特性使我们的方法能够自动确定手术的哪些部分对技能评估影响最大，从而使我们能够解释手术技能分类，并为外科医生提供一种新的个性化反馈技术。我们相信这种可解释的机器学习模型可以集成到“手术室2.0”中，并支持新手外科医生提高他们的技能，最终成为专家。
Purpose Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of trainees' surgical skills in order to improve surgical practice. Methods In this paper, we designed a convolutional neural network (CNN) to classify surgical skills by extracting latent patterns in the trainees' motions performed during robotic surgery. The method is validated on the JIGSAWS dataset for two surgical skills evaluation tasks: classification and regression. Results Our results show that deep neural networks constitute robust machine learning models that are able to reach new competitive state-of-the-art performance on the JIGSAWS dataset. While we leveraged from CNNs' efficiency, we were able to minimize its black-box effect using the class activation map technique. Conclusions This characteristic allowed our method to automatically pinpoint which parts of the surgery influenced the skill evaluation the most, thus allowing us to explain a surgical skill classification and provide surgeons with a novel personalized feedback technique. We believe this type of interpretable machine learning model could integrate within "Operation Room 2.0" and support novice surgeons in improving their skills to eventually become experts.