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：在腹腔镜视频中自动检测与追踪手术器械的能力有望推动变革性手术干预。评估手术表现与效率、识别熟练的器械使用与编排、规划手术室资源的操作与后勤环节，仅是其中受益的若干应用场景。然而，获取训练机器学习模型所需的标注数据以识别和定位手术器械是一项艰巨任务。逐帧标注边界框既繁琐又耗时，但为获得鲁棒训练，仍需采集涵盖多种手术器械与术式的大规模数据。此外，标注人员需持续接受培训以跟进手术器械的创新。但在机器人辅助手术中，诸如器械安装与拆卸的时间戳等潜在信息数据可通过程序化方式获取。仅依赖器械安装数据的能力将显著降低训练稳健器械追踪模型的工作量。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。目标是将器械存在数据作为弱标签，训练用于在视频帧中通过边界框检测并定位器械的机器学习模型。本文呈现了本次挑战赛的结果及各团队的诸多努力，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。本次挑战赛使用的训练数据包含24,695个带器械存在标签的视频片段，现已公开，可通过https://console.cloud.google.com/storage/browser/isi-surgtoolloc-2022 获取。