用超声视频训练模型，自动识别高血压导致的血管损伤。
Deep Learning for Cardiovascular Risk Assessment: Proxy Features from Carotid Sonography as Predictors of Arterial Damage
- 用VideoMAE模型分析颈动脉超声视频，提取视觉特征。
- 在3.1万+视频上验证，分类准确率达75.7%。
- 适合心血管风险早期筛查，无需复杂设备。
本研究将高血压作为个体血管损伤的指标，通过机器学习技术识别该损伤，为重大心血管事件提供早期风险预警，并揭示患者整体动脉状况。为此，我们对原始用于视频分类的VideoMAE深度学习模型进行微调，应用于超声成像领域。模型基于来自歌德堡健康研究（Gutenberg Health Study）的超过31,000例颈动脉超声视频数据集（涵盖15,010名参与者）进行训练与测试。该方法可有效区分高血压与非高血压个体，验证准确率达75.7%，作为视觉性动脉损伤的代理指标。结果表明，该模型能捕捉到反映个体心血管健康的关键视觉特征。
原文摘要 · Abstract (English)
In this study, hypertension is utilized as an indicator of individual vascular damage. This damage can be identified through machine learning techniques, providing an early risk marker for potential major cardiovascular events and offering valuable insights into the overall arterial condition of individual patients. To this end, the VideoMAE deep learning model, originally developed for video classification, was adapted by finetuning for application in the domain of ultrasound imaging. The model was trained and tested using a dataset comprising over 31,000 carotid sonography videos sourced from the Gutenberg Health Study (15,010 participants), one of the largest prospective population health studies. This adaptation facilitates the classification of individuals as hypertensive or non-hypertensive (75.7% validation accuracy), functioning as a proxy for detecting visual arterial damage. We demonstrate that our machine learning model effectively captures visual features that provide valuable insights into an individual's overall cardiovascular health.
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