Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, yet early risk detection is often limited by available diagnostics. Carotid ultrasound, a non-invasive and widely accessible modality, encodes rich structural and hemodynamic information that is largely untapped. Here, we present a machine learning (ML) framework that extracts clinically meaningful representations of vascular damage (VD) from carotid ultrasound videos, using hypertension as a weak proxy label. The model learns robust features that are biologically plausible, interpretable, and strongly associated with established cardiovascular risk factors, comorbidities, and laboratory measures. High VD stratifies individuals for myocardial infarction, cardiac death, and all-cause mortality, matching or outperforming conventional risk models such as SCORE2. Explainable AI analyses reveal that the model relies on vessel morphology and perivascular tissue characteristics, uncovering novel functional and anatomical signatures of vascular damage. This work demonstrates that routine carotid ultrasound contains far more prognostic information than previously recognized. Our approach provides a scalable, non-invasive, and cost-effective tool for population-wide cardiovascular risk assessment, enabling earlier and more personalized prevention strategies without reliance on laboratory tests or complex clinical inputs.

翻译：心血管疾病（CVDs）仍是全球范围内主要的死亡原因，然而早期风险检测常受限于现有的诊断方法。颈动脉超声作为一种无创且广泛可及的检查方式，编码了丰富的结构和血流动力学信息，这些信息在很大程度上尚未被充分利用。本文提出一种机器学习（ML）框架，该框架利用高血压作为弱代理标签，从颈动脉超声视频中提取具有临床意义的血管损伤（VD）表征。该模型学习到的特征具有生物学合理性、可解释性，并且与既定的心血管风险因素、合并症及实验室指标密切相关。高VD评分可对心肌梗死、心源性死亡及全因死亡风险进行个体分层，其性能匹配或优于SCORE2等传统风险模型。可解释性人工智能分析表明，该模型依赖于血管形态学和血管周围组织特征，从而揭示了血管损伤的新功能与解剖学标志。这项工作证明，常规颈动脉超声所包含的预后信息远超既往认知。我们的方法为大规模人群心血管风险评估提供了一种可扩展、无创且经济高效的工具，能够在无需依赖实验室检测或复杂临床输入的情况下，实现更早、更个性化的预防策略。