CardioSyntax: End-to-End SYNTAX Score Prediction - Dataset Benchmark and Method

Alexander Ponomarchuk, Ivan Kruzhilov, Gleb Mazanov, Ruslan Utegenov, Artem Shadrin, Galina Zubkova, Ivan Bessonov, Pavel Blinov; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5873-5883

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.

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[bibtex]
@InProceedings{Ponomarchuk_2025_WACV, author = {Ponomarchuk, Alexander and Kruzhilov, Ivan and Mazanov, Gleb and Utegenov, Ruslan and Shadrin, Artem and Zubkova, Galina and Bessonov, Ivan and Blinov, Pavel}, title = {CardioSyntax: End-to-End SYNTAX Score Prediction - Dataset Benchmark and Method}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {5873-5883} }