Intuitive Surgical SurgToolLoc Challenge Results: 2022-2023
This work addresses the need for better machine learning models to enhance robotic-assisted surgery, though it is incremental as it builds on existing challenges and datasets.
The paper presents results from the Intuitive Surgical SurgToolLoc Challenge, which tackled the problem of surgical tool localization in robotic-assisted surgery, with the community achieving improved localization accuracy through annual competitions.
Robotic assisted (RA) surgery promises to transform surgical intervention. Intuitive Surgical is committed to fostering these changes and the machine learning models and algorithms that will enable them. With these goals in mind we have invited the surgical data science community to participate in a yearly competition hosted through the Medical Imaging Computing and Computer Assisted Interventions (MICCAI) conference. With varying changes from year to year, we have challenged the community to solve difficult machine learning problems in the context of advanced RA applications. Here we document the results of these challenges, focusing on surgical tool localization (SurgToolLoc). The publicly released dataset that accompanies these challenges is detailed in a separate paper arXiv:2501.09209 [1].