The ability to automatically detect and track surgical instruments in endoscopic videos can enable transformational interventions. Assessing surgical performance and efficiency, identifying skilled tool use and choreography, and planning operational and logistical aspects of OR resources are just a few of the applications that could benefit. Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task. Annotating bounding boxes frame-by-frame is tedious and time-consuming, yet large amounts of data with a wide variety of surgical tools and surgeries must be captured for robust training. Moreover, ongoing annotator training is needed to stay up to date with surgical instrument innovation. In robotic-assisted surgery, however, potentially informative data like timestamps of instrument installation and removal can be programmatically harvested. The ability to rely on tool installation data alone would significantly reduce the workload to train robust tool-tracking models. With this motivation in mind we invited the surgical data science community to participate in the challenge, SurgToolLoc 2022. The goal was to leverage tool presence data as weak labels for machine learning models trained to detect tools and localize them in video frames with bounding boxes. We present the results of this challenge along with many of the team's efforts. We conclude by discussing these results in the broader context of machine learning and surgical data science. The training data used for this challenge consisting of 24,695 video clips with tool presence labels is also being released publicly and can be accessed at https://console.cloud.google.com/storage/browser/isi-surgtoolloc-2022.

翻译：在内窥镜视频中自动检测和跟踪手术器械的能力可推动变革性干预措施。评估手术表现与效率、识别熟练的工具使用与编排、规划手术室资源的操作与后勤环节，仅是其中可能受益的几项应用。然而，获取训练机器学习模型以识别和定位手术工具所需的标注数据是一项艰巨任务。逐帧标注边界框繁琐且耗时，但为实现鲁棒训练，必须采集涵盖多种手术工具与术式的大量数据。此外，标注人员需持续接受培训以跟上手术器械创新的步伐。然而，在机器人辅助手术中，诸如器械安装与移除时间戳等潜在信息数据可通过编程方式采集。仅依赖工具安装数据的能力将大幅降低训练鲁棒工具追踪模型的工作量。基于这一动机，我们邀请手术数据科学界参与2022年SurgToolLoc挑战赛。其目标是将工具存在数据作为弱标签，用于训练以边界框形式检测并定位视频帧中工具的机器学习模型。本文呈现了该挑战赛的结果及众多参赛团队的成果，并在机器学习与手术数据科学的更广泛背景下对结果进行了讨论。本挑战赛使用的训练数据（包含24,695个带工具存在标签的视频片段）也已公开，可通过https://console.cloud.google.com/storage/browser/isi-surgtoolloc-2022 获取。