Abstract
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.
Abstract (translated)
在这项研究中，高血压被用作个体血管损伤的指标。这种损伤可以通过机器学习技术识别出来，为潜在的重大心血管事件提供早期风险标志，并对个人患者的动脉状况提供有价值的见解。为此，原本用于视频分类的VideoMAE深度学习模型经过微调后应用于超声成像领域。该模型使用来自古腾堡健康研究（15,010名参与者）的大于31,000个颈动脉超声影像视频的数据集进行训练和测试。这项适应使得能够将个体分类为高血压或非高血压患者（验证准确率为75.7%），从而作为检测视觉动脉损伤的代理工具。我们证明了我们的机器学习模型能够有效地捕捉到提供有关个人整体心血管健康状况有价值见解的视觉特征。
URL
https://arxiv.org/abs/2504.06680
