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The ability to automatically detect and track surgical instruments inendoscopic videos can enable transformational interventions. Assessing surgicalperformance and efficiency, identifying skilled tool use and choreography, andplanning operational and logistical aspects of OR resources are just a few ofthe applications that could benefit. Unfortunately, obtaining the annotationsneeded to train machine learning models to identify and localize surgical toolsis a difficult task. Annotating bounding boxes frame-by-frame is tedious andtime-consuming, yet large amounts of data with a wide variety of surgical toolsand surgeries must be captured for robust training. Moreover, ongoing annotatortraining is needed to stay up to date with surgical instrument innovation. Inrobotic-assisted surgery, however, potentially informative data like timestampsof instrument installation and removal can be programmatically harvested. Theability to rely on tool installation data alone would significantly reduce theworkload to train robust tool-tracking models. With this motivation in mind weinvited the surgical data science community to participate in the challenge,SurgToolLoc 2022. The goal was to leverage tool presence data as weak labelsfor machine learning models trained to detect tools and localize them in videoframes with bounding boxes. We present the results of this challenge along withmany of the team's efforts. We conclude by discussing these results in thebroader context of machine learning and surgical data science. The trainingdata used for this challenge consisting of 24,695 video clips with toolpresence labels is also being released publicly and can be accessed athttps://console.cloud.google.com/storage/browser/isi-surgtoolloc-2022.

Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge