深度学习预测左心房结构和功能从12心电图
medRxiv : the preprint server for health sciences
|November 19, 2025
概括
在心电图上训练的人工智能工具 (ECG-AI) 可以检测心房心脏病,这是心房动 (AF) 的前体. 这种可访问的技术可以预测AF和相关的心血管风险,优于传统方法.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 心房心脏病,以心房结构和功能异常为特征,往往先于心房动 (AF) 和其他心血管问题.
- 目前的检测方法依赖于昂贵且难以获得的心脏成像技术.
研究的目的:
- 开发和验证一个深度学习模型 (ECG-AI),使用12导电心电图 (ECG) 预测左心房结构和功能.
- 评估ECG-AI在识别AF和相关心血管并发症高风险人群中的有效性.
主要方法:
- 一个深度学习模型 (ECG-AI) 经过训练,使用来自21,749名个人的12导电图,与心脏磁共振成像 (CMR) 扫描相结合.
- 该模型对心房心脏病的预测在两个外部队列中得到验证,将其性能与成像测量和临床风险因素进行比较.
- 通过使用14天的心脏监测在查人群中评估了ECG-AI预测亚临床AF的能力.
主要成果:
- 对心房心脏病的ECG-AI测量显示,即使对临床因素进行调整后,与新发动的AF,心力衰竭和缺血性中风有很强的关联.
- 该模型在预测这些心血管事件方面表现优于传统的成像测量和临床风险因素.
- 根据ECG-AI的预测,左心房体积增加与心血管血栓性中风风险增加66%的标准偏差有关.
- 在查环境中,ECG-AI与临床风险预测工具相比,更好地预测亚临床AF.
结论:
- 电脑心电图-人工智能 (ECG-AI) 是一种廉价且易于使用的工具,用于识别心房心脏病症和AF风险高的人.
- 这种人工智能驱动的方法为心血管风险评估和早期检测AF提供了传统成像的有希望的替代方案.
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