从心电图估计上升的大动脉直径
medRxiv : the preprint server for health sciences
|September 2, 2025
概括
深度学习模型 (ECGAI-TAA) 可以从心电图 (ECG) 信号中估计上升的大动脉直径,提供超越传统临床因素的见解. 这一发现可能有助于非侵入性地识别大动脉扩张风险.
科学领域:
- 心脏病学
- 医学成像
- 人工智能
背景情况:
- 动脉直径上升是一个关键的心血管参数.
- 评估大动脉直径的非侵入性方法对心血管健康监测有价值.
- 电心电图 (ECG) 信号包含丰富的生理信息.
研究的目的:
- 开发和验证深度学习模型 (ECGAI-TAA) 用于估计使用12导电图数据的上升性大动脉直径.
- 调查心电图衍生的大动脉直径估计是否独立于传统的临床因素.
- 探索基于心电图分析的潜力,以识别患有大动脉扩张风险较高的人.
主要方法:
- 对69,173名英国生物库参与者的分析,将MRI上升的大动脉直径与心电图信号相对应.
- 在10秒,500Hz,12导电图数据上训练1D卷积神经网络 (ECGAI-TAA).
- 在5191名参与者的内部验证中,评估了异常的解释和与大动脉扩张值 (4.0厘米) 的关联.
主要成果:
- 根据ECGAI-TAA模型,上升性大动脉直径的差异为31%.
- 这种预测能力并不能完全由年龄,性别或血压等临床因素来解释.
- 在该模型的最高2.5%的个体显示出大动脉扩张的几率几乎是其他个体的16倍.
结论:
- 通过ECGAI-TAA深度学习模型,可以从12导心电图中部分估计上升的大动脉直径.
- 通过心电图推导出大动脉直径的估计,为非侵入性心血管风险评估提供了潜力.
- 需要进一步的研究来验证这些发现作为外部验证的风险评分.
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