# Real-Time Coronary Artery Dominance Classification from Angiographic Images Using Advanced Deep Video Architectures

## TL;DR

This paper introduces a deep learning system that automatically identifies coronary artery dominance from angiograms using video-based models, improving accuracy and reducing manual steps.

## Contribution

The novel contribution is an integrated video-based deep learning framework that classifies coronary dominance without requiring manual separation of RCA and LCA angiograms.

## Key findings

- Transformer-based models achieved higher accuracy than convolution-based methods in classifying coronary dominance.
- VideoMAEv2 achieved the highest test accuracy of 92.89% among the evaluated models.
- The framework eliminates the need for manual separation of right and left coronary artery angiograms during preprocessing.

## Abstract

Background/Objectives: The automatic identification of coronary artery dominance holds critical importance for clinical decision-making in cardiovascular medicine, influencing diagnosis, treatment planning, and risk stratification. Traditional classification methods rely on the manual visual interpretation of coronary angiograms. However, current deep learning approaches typically classify right and left coronary artery angiograms separately. This study aims to develop and evaluate an integrated video-based deep learning framework for classifying coronary dominance without distinguishing between RCA and LCA angiograms. Methods: Three advanced video-based deep learning models—Temporal Segment Networks (TSNs), Video Swin Transformer (VST), and VideoMAEv2—were implemented using the MMAction2 framework. These models were trained and evaluated on a large dataset derived from a publicly available source. The integrated approach processes entire angiographic video sequences, eliminating the need for separate RCA and LCA identification during preprocessing. Results: The proposed framework demonstrated strong performance in classifying coronary dominance. The best test accuracies achieved using TSNs, Video Swin Transformer, and VideoMAEv2 were 87.86%, 92.12%, and 92.89%, respectively. Transformer-based models showed superior accuracy compared to convolution-based methods, highlighting their effectiveness in capturing spatial–temporal patterns in angiographic videos. Conclusions: This study introduces a unified video-based deep learning approach for coronary dominance classification, eliminating manual arterial branch separation and reducing preprocessing complexity. The results indicate that transformer-based models, particularly VideoMAEv2, offer highly accurate and clinically feasible solutions, contributing to the development of objective and automated diagnostic tools in cardiovascular imaging.