  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.
