Topic: Artificial Intelligence in Cardiovascular Aging and Disease

Cardiovascular disease (CVD) stands as a leading cause of mortality and morbidity worldwide. Cardiovascular aging, characterized by structural and functional deterioration of the cardiovascular system, is a critical precursor to the onset and progression of CVD. Despite significant advances in clinical and basic research, challenges remain in the early detection, precise diagnosis, effective intervention, and accurate prognosis of cardiovascular aging and related diseases. Artificial intelligence (AI) has emerged as a transformative force in healthcare, revolutionizing the landscape of cardiovascular research and clinical practice. AI technologies exhibit remarkable capabilities in analyzing large-scale, multidimensional data such as clinical records, imaging data, omics data, and wearable sensor data. These technologies enable the identification of novel biomarkers of cardiovascular aging, the development of accurate predictive models for disease risk stratification, the optimization of personalized treatment regimens, and the enhancement of clinical decision making. However, despite the promising potential of AI applications in cardiovascular aging and disease, several gaps and challenges remain.

This Special Issue aims to provide a platform for researchers and practitioners to share new findings and insights on the integration of AI in cardiovascular aging and disease. Topics of interest include, but are not limited to:

AI-enabled identification of biomarkers in cardiovascular aging;

AI models for early detection and diagnosis of cardiovascular diseases;

AI-driven prediction models for cardiovascular disease risk assessment, progression, and prognosis;

Application of AI in personalized treatment optimization;

AI-based analysis of multi-omics data to unravel the molecular mechanisms of cardiovascular aging;

AI-powered analysis of medical imaging data for cardiovascular aging and disease evaluation;

Integration of wearable device data and AI for real-time monitoring and management of cardiovascular health in the elderly;

AI interpretability, data quality, and privacy issues in cardiovascular research and clinical practice;

Clinical translation of AI technologies in cardiovascular aging and disease;

AI applications in early warning of cardiovascular perioperative risk.