AI-Driven Advances in Vascular Aging Research: From Mechanisms to Precision Medicine.
TL;DR
This review provides a comprehensive overview of current AI applications in vascular aging, spanning from basic mechanistic research to clinical risk prediction and discusses future directions and key challenges, including the need for external validation, algorithmic fairness, domain adaptation, and model interpretability.
Abstract
Vascular aging is a fundamental contributor to the development of chronic diseases and has emerged as a critical focus in biomedical research. With the rapid advancement of artificial intelligence (AI), new opportunities have arisen to enhance the precision and efficiency of vascular aging studies. AI techniques, particularly those applied to large-scale multi-omics and medical imaging data, enable the identification of novel biomarkers and the development of robust models to quantify the rate and extent of vascular aging. For example, AI has been successfully deployed for multi-omics biomarker discovery, automated quantification of coronary plaque and calcium scoring from CT imaging, and the development of vascular aging clocks based on retinal images or photoplethysmography (PPG). These approaches facilitate early detection, individualized risk assessment, and potential intervention strategies. This review provides a comprehensive overview of current AI applications in vascular aging, spanning from basic mechanistic research to clinical risk prediction. It also discusses future directions and key challenges, including the need for external validation, algorithmic fairness, domain adaptation, and model interpretability, emphasizing the transformative role of AI in advancing precision medicine for vascular health.