Coronary Artery Disease

Coronary artery disease (CAD), a leading cause of death globally, is the focus of intense research aimed at improving early detection and risk stratification. Current research utilizes various machine learning models, including deep learning architectures like convolutional neural networks (CNNs), transformers, and ensemble methods, to analyze diverse data sources such as coronary angiography, computed tomography angiography (CCTA), and electrocardiograms (ECGs) for improved diagnostic accuracy and personalized treatment strategies. These advancements hold significant promise for enhancing CAD diagnosis, enabling more timely interventions, and ultimately reducing morbidity and mortality associated with this prevalent condition. The development of large, publicly available datasets is also crucial for advancing the field and ensuring the reproducibility of research findings.