The reviewed record of science sign in
Pith

arxiv: 2212.04448 · v1 · pith:QDBSO6IC · submitted 2022-12-08 · cs.CV

Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge

Reviewed by Pithpith:QDBSO6ICopen to challenge →

classification cs.CV
keywords surgicalfeedbackobjectiveskillsadvancesassessmentchallengedataset
0
0 comments X
read the original abstract

Timely and effective feedback within surgical training plays a critical role in developing the skills required to perform safe and efficient surgery. Feedback from expert surgeons, while especially valuable in this regard, is challenging to acquire due to their typically busy schedules, and may be subject to biases. Formal assessment procedures like OSATS and GEARS attempt to provide objective measures of skill, but remain time-consuming. With advances in machine learning there is an opportunity for fast and objective automated feedback on technical skills. The SimSurgSkill 2021 challenge (hosted as a sub-challenge of EndoVis at MICCAI 2021) aimed to promote and foster work in this endeavor. Using virtual reality (VR) surgical tasks, competitors were tasked with localizing instruments and predicting surgical skill. Here we summarize the winning approaches and how they performed. Using this publicly available dataset and results as a springboard, future work may enable more efficient training of surgeons with advances in surgical data science. The dataset can be accessed from https://console.cloud.google.com/storage/browser/isi-simsurgskill-2021.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Surgical Visual Understanding (SurgVU) Dataset

    cs.CV 2025-01 unverdicted novelty 5.0

    Releases the SurgVU dataset of surgical videos and labels to enable machine learning research in surgical data science.

  2. Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-2025

    cs.CV 2023-05 unverdicted novelty 2.0

    The paper summarizes results from the SurgToolLoc and SurgVU challenges held at MICCAI conferences from 2022 to 2025.