使用深度学习方法从未校准的,非侵入性的压力波形评估血管年龄:AI-VascularAge模型
Gary F Mitchell1, Jian Rong2, Martin G Larson2,3
1Cardiovascular Engineering, Inc, Norwood, MA (G.F.M., T.J.K., J.D.G.).
Hypertension (Dallas, Tex. : 1979)
|October 30, 2023
概括
人工智能-血管年龄 (AI-VA) 从动脉波形有效估计血管年龄,预测心血管疾病风险. 这种由人工智能驱动的方法为评估血管健康提供了一种新的非侵入性方法.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 大动脉硬度,通过带-大腿脉冲波速度来衡量,表明血管年龄和心血管疾病 (CVD) 风险.
- 测量大动脉硬度是具有挑战性的,并且对分析动脉压力波形的最佳方法进行了辩论.
研究的目的:
- 开发和验证一种使用卷积神经网络 (CNN) 的人工智能 (AI) 模型,从动脉度波形估计血管年龄.
- 评估AI衍生血管年龄 (AI-VA) 对发生心血管疾病事件的预测能力.
主要方法:
- 一个CNN被训练使用手臂,半径和动脉度波形作为输入,负的逆动脉-大腿脉冲波速作为目标.
- 该模型在冰岛的大型队列上进行了训练,并使用Framingham心脏研究数据集进行了验证.
- 线性回归被用于将预测重新调整为AI-VA,代表相当的血管年龄.
主要成果:
- 在AI-VascularAge模型中,在训练和验证数据集 (R2=0.60-0.64) 中,AI-VascularAge模型在负反旋-大腿脉冲波速的预测准确度很好.
- 来自臂动脉波形的AI-VA与发生心血管疾病,冠心病和心力衰竭有显著的关联,即使在调整传统心血管疾病风险因素后也是如此.
- 观察到类似的协会AI-VA估计从动脉和辐射波形.
结论:
- 源自CNN的AI-VA是血管健康的强大而有力的指标.
- AI-VA是评估不同人群中心血管疾病风险的宝贵工具.
- 这种人工智能驱动的方法为评估血管衰老和心血管疾病风险提供了一个有希望的非侵入性方法.
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