CardioSyntax: end-to-end SYNTAX score prediction -- dataset, benchmark and method

TL;DR

This paper introduces CardioSyntax, a comprehensive dataset and a novel end-to-end method for automatically estimating SYNTAX scores from coronary angiography, achieving promising predictive accuracy.

Contribution

It provides the first large-scale dataset with multi-view angiography samples and a new automatic method for SYNTAX score prediction.

Findings

R2 of 0.51 in score prediction

77.3% accuracy in zero score classification

First dataset with multi-view angiography samples

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 3,018 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.