Deep Learning (DL) has achieved automatic and objective assessment of surgical skills. However, DL models are data-hungry and restricted to their training domain. This prevents them from transitioning to new tasks where data is limited. Hence, domain adaptation is crucial to implement DL in real life. Here, we propose a meta-learning model, A-VBANet, that can deliver domain-agnostic surgical skill classification via one-shot learning. We develop the A-VBANet on five laparoscopic and robotic surgical simulators. Additionally, we test it on operating room (OR) videos of laparoscopic cholecystectomy. Our model successfully adapts with accuracies up to 99.5% in one-shot and 99.9% in few-shot settings for simulated tasks and 89.7% for laparoscopic cholecystectomy. For the first time, we provide a domain-agnostic procedure for video-based assessment of surgical skills. A significant implication of this approach is that it allows the use of data from surgical simulators to assess performance in the operating room.

翻译：深度学习（DL）已实现了手术技能的自动化和客观评估。然而，深度学习模型对数据需求量大，且受限于其训练域。这阻碍了它们向数据有限的新任务迁移。因此，域适应对于在实际生活中部署深度学习至关重要。在此，我们提出了一种元学习模型A-VBANet，可通过单样本学习实现域无关的手术技能分类。我们在五种腹腔镜和机器人手术模拟器上开发了A-VBANet。此外，我们还在腹腔镜胆囊切除术的手术室视频上对其进行了测试。我们的模型成功实现了域适应，在模拟任务中单样本设置下准确率高达99.5%，少样本设置下达99.9%，在腹腔镜胆囊切除术中达89.7%。我们首次为基于视频的手术技能评估提供了域无关的流程。该方法的一个重要意义在于，它允许利用手术模拟器中的数据来评估手术室中的表现。