IRIMAS, Université Haute Alsace, 12 Rue des Frères Lumière, 68093, Mulhouse, France.
Int J Comput Assist Radiol Surg. 2019 Sep;14(9):1611-1617. doi: 10.1007/s11548-019-02039-4. Epub 2019 Jul 30.
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.
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.
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.
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.
资深外科医生对经验较少的受训者进行手动反馈是一项费力的任务，既昂贵又耗时，且容易主观。随着每年手术数量的增加，人们前所未有地需要对受训者的手术技能进行准确、客观和自动的评估，以提高手术水平。
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中受训者动作的潜在模式来对手术技能进行分类。该方法在 JIGSAWS 数据集上针对两个手术技能评估任务进行了验证：分类和回归。
我们的结果表明，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最先进性能。虽然我们利用了 CNN 的效率，但我们能够通过类激活图技术最小化其黑盒效应。
这种特性使我们的方法能够自动确定哪些手术部位对技能评估影响最大，从而使我们能够对手术技能进行分类，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，并帮助新手外科医生提高技能，最终成为专家。
