Federated Graph-Transformer Network for Coronary Artery Disease Severity Grading from X-Ray Coronary Angiography

Automated assessment of coronary artery disease (CAD) severity from invasive X-ray angiography is important for diagnostic accuracy, but there are limitations due to limited label data and privacy issues in multi-institutional collaboration. This research proposes a Federated Graph-Transformer Network (FGTN) that models coronary vessel compositions as graphs and uses a transformer unit of measurement to encode global anatomic circumstances for severity scaling. The publicly available X-ray angiography images and SYNTAX-Score dataset will be used, consisting of 232 X-ray coronary angiography images with analogous clinically calculated SYNTAX tons and angiographic factors from 231 patients, manually annotated by a competent cardiologist. The vascular tree is a primary segment that transforms inside the node-edge graph representing bifurcation and vessel sections, continuing topological features, and then processes by graph convolutions integrated with transformer self-attention to capture simultaneously the local stenosis features and global vessel relationships. A Horizontal Federated Learning Strategy allowing collaborative model training on clinical sites without sharing raw data. The intended FGTN achieved overall accuracy of 99.4%, precision of 97.6%, recall of 98.8%, and F1-score of 98.2%, exceeding the usual CNNs, Attention-UNet, and Capsule Connection baselines by a margin of 4–7%. For non-obstructive, mild, moderate, and severe stenosis classes, the AUC values were 0.98, 0.97, 0.96, and 0.95, respectively. Moreover, the Federated Learning framework shows firm convergence with lower, compared to 1.8% performance degradation, when compared to centralized training, and confirms robustness via heterogeneous data distribution. These results show that the proposed solution automatically calculates the CAD severity grading from coronary angiography images.