CardioSyntax: End-to-End SYNTAX Score Prediction - Dataset Benchmark and Method
Abstract
The SYNTAX score has become a widely used measure of coronary disease severity crucial in selecting the optimal mode of the revascularization procedure. This paper introduces a new medical regression and classification problem -- automatically estimating SYNTAX score from coronary angiography. Our study presents a comprehensive CardioSYNTAX dataset of 3018 patients for the SYNTAX score estimation and coronary dominance classification. The dataset features a balanced distribution of individuals with zero and non-zero scores. This dataset includes a first-of-its-kind complete coronary angiography samples captured through a multi-view X-ray video allowing one to observe coronary arteries from multiple perspectives. Furthermore we present a novel fully automatic end-to-end method for estimating the SYNTAX. For such a difficult task we have achieved a solid coefficient of determination R2 of 0.51 in score value prediction and 77.3% accuracy for zero score classification.
Cite
Text
Ponomarchuk et al. "CardioSyntax: End-to-End SYNTAX Score Prediction - Dataset Benchmark and Method." Winter Conference on Applications of Computer Vision, 2025.Markdown
[Ponomarchuk et al. "CardioSyntax: End-to-End SYNTAX Score Prediction - Dataset Benchmark and Method." Winter Conference on Applications of Computer Vision, 2025.](https://mlanthology.org/wacv/2025/ponomarchuk2025wacv-cardiosyntax/)BibTeX
@inproceedings{ponomarchuk2025wacv-cardiosyntax,
title = {{CardioSyntax: End-to-End SYNTAX Score Prediction - Dataset Benchmark and Method}},
author = {Ponomarchuk, Alexander and Kruzhilov, Ivan and Mazanov, Gleb and Utegenov, Ruslan and Shadrin, Artem and Zubkova, Galina and Bessonov, Ivan and Blinov, Pavel},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2025},
pages = {5873-5883},
url = {https://mlanthology.org/wacv/2025/ponomarchuk2025wacv-cardiosyntax/}
}