人工智能驱动的血管衰老研究进展：从机制到精准医学

摘要(中文译文)

血管衰老是慢性疾病发生发展的基础性因素，已成为生物医学研究的重点领域。随着人工智能（AI）的快速发展，提升血管衰老研究的精准性和效率迎来了新的机遇。AI技术，尤其是应用于大规模多组学和医学影像数据的技术，能够识别新型生物标志物并开发稳健模型以量化血管衰老的速率和程度。例如，AI已成功应用于多组学生物标志物发现、基于CT影像的冠状动脉斑块自动量化和钙化评分，以及基于视网膜图像或光电容积脉搏波（PPG）的血管衰老时钟开发。这些方法有助于早期检测、个体化风险评估和潜在干预策略的制定。本综述全面概述了当前AI在血管衰老中的应用，涵盖从基础机制研究到临床风险预测。同时讨论了未来方向和关键挑战，包括外部验证、算法公平性、域适应和模型可解释性的需求，强调AI在推进血管健康精准医学方面的变革性作用。

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.

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