hfawaz/ijcars19
Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks
This project helps surgical educators and researchers automatically assess surgical skill by analyzing motion data from surgical tools. It takes raw kinematic data (e.g., from robotic surgery systems) as input and outputs an evaluation of skill level (e.g., expert vs. novice) or specific performance metrics. It's designed for those involved in surgical training and performance analysis.
No commits in the last 6 months.
Use this if you need an automated and objective way to evaluate surgical trainees' performance based on their movement patterns during simulated or real surgical tasks.
Not ideal if you need to evaluate surgical skills using qualitative observational assessments or video analysis without kinematic data.
Stars
10
Forks
6
Language
Python
License
GPL-3.0
Category
Last pushed
Aug 21, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/hfawaz/ijcars19"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
BiaPyX/BiaPy
Open source Python library for building bioimage analysis pipelines
Abe404/root_painter
RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation
junlabucsd/napari-mm3
Mother machine image analysis through napari
Dootmaan/MT-UNet
Official Code for *Mixed Transformer UNet for Medical Image Segmentation*
chanzuckerberg/napari-hub
Discover, install, and share napari plugins