Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-2025

TL;DR

This paper reports on the results of a series of MICCAI challenges from 2022 to 2025 focused on machine learning tasks for robotic-assisted surgery, including tool localization and visual understanding.

Contribution

It presents the outcomes of annual competitions that advance surgical data science through challenging machine learning problems in robotic surgery.

Findings

Community-developed models improved tool localization accuracy.

Advancements in visual understanding enhanced surgical scene interpretation.

Benchmark datasets facilitated progress in surgical AI research.

Abstract

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) and surgical visual understanding (SurgVU). The publicly released dataset that accompanies these challenges is detailed in a separate paper arXiv:2501.09209 [1].