Surgical robotics holds much promise for improving patient safety and clinician experience in the Operating Room (OR). However, it also comes with new challenges, requiring strong team coordination and effective OR management. Automatic detection of surgical activities is a key requirement for developing AI-based intelligent tools to tackle these challenges. The current state-of-the-art surgical activity recognition methods however operate on image-based representations and depend on large-scale labeled datasets whose collection is time-consuming and resource-expensive. This work proposes a new sample-efficient and object-based approach for surgical activity recognition in the OR. Our method focuses on the geometric arrangements between clinicians and surgical devices, thus utilizing the significant object interaction dynamics in the OR. We conduct experiments in a low-data regime study for long video activity recognition. We also benchmark our method againstother object-centric approaches on clip-level action classification and show superior performance.

翻译：手术机器人技术有望提升手术室中患者安全性和临床操作体验，但也带来了团队协作与手术室管理的全新挑战。手术活动的自动检测是开发基于人工智能的智能工具以应对这些挑战的关键需求。然而，当前最先进的手术活动识别方法仍依赖基于图像的表示，并且需要大规模标注数据集，而此类数据的收集既耗时又昂贵。本研究提出了一种新颖的样本高效、基于对象的手术室活动识别方法。该方法聚焦于临床医生与手术器械之间的几何排列关系，充分利用手术室中显著的对象交互动态特性。我们在长视频活动识别的数据稀缺场景下开展实验，同时与其它以对象为中心的方法在片段级动作分类任务上进行基准测试，结果显示本方法具有更优性能。