AngioCAD: A public x-ray angiography dataset and an adaptive fusion framework for stenosis detection

Coronary artery disease (CAD) is a leading cause of mortality worldwide, underscoring the need for accurate and timely diagnosis. While X-ray coronary angiography remains the clinical gold standard for detecting stenosis, its manual interpretation is labor-intensive and prone to inter-observer variability. Many existing artificial intelligence-based approaches rely on limited frame-level datasets that lack temporal continuity, artery-specific annotations, and corresponding clinical context, thereby limiting their effectiveness in real-world applications.

To address these challenges, we introduce AngioCAD, a publicly available dataset comprising angiographic video sequences and structured clinical data from 413 patients. Each case includes detailed stenosis annotations for every coronary artery, such as 100% stenosis in the proximal segment of the right coronary artery, along with demographic and laboratory information. This dataset supports a broad range of CAD-related tasks, including diagnosis, view classification, and stenosis detection, through both image- and attribute-based analysis. We further propose a deep learning framework for stenosis detection based on video modeling. The model integrates extracted features from two convolutional neural networks via an adaptive fusion module that learns attention weights (α) to prioritize the most informative feature stream for each case.

The framework achieves superior performance across multiple evaluation metrics, including F1-score and PR-AUC. Furthermore, we show that incorporating discretized and normalized clinical attributes improves classification performance in classical models, with a polynomial-kernel SVM achieving an F1-score of 89.78%.

These findings highlight the potential of the AngioCAD dataset and adaptive video modeling for improving automated CAD and stenosis detection.