用人工智能增强的心电图学来预测反性膜心脏病:一项国际研究
Yixiu Liang1,2, Arunashis Sau2,3, Boroumand Zeidaabadi2
1Department of Cardiology, Zhongshan Hospital of Fudan University, Shanghai, Institute of Cardiovascular Diseases, National Clinical Research Centre for Interventional Medicine, Shanghai 200032, China.
European heart journal
|July 16, 2025
概括
人工智能增强型心电图 (AI-ECG) 模型可以诊断和预测未来中度或重度复发性膜心脏病 (rVHD). 这些AI-ECG模型有望指导风险患者的早期检测和干预.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 膜心脏病 (VHD) 显著导致发病率和死亡率.
- 在VHD的早期干预可以改善患者的结果.
- 为VHD开发先进的诊断工具至关重要.
研究的目的:
- 开发人工智能增强型心电图 (AI-ECG) 模型.
- 诊断和预测未来的中度或重度吐性VHDs (rVHDs),包括心肌吐 (MR),三管吐 (TR) 和大动脉吐 (AR).
主要方法:
- 人工智能-心电图模型是使用残余卷积神经网络与离散时间生存损失函数开发的.
- 训练了988,618个ECG和400,882名患者的心电图和心声图对的大数据集.
- 在34214名患者的单独队列中进行了外部验证.
主要成果:
- AI-ECG模型准确地预测了未来显著的MR,AR和TR.
- 高风险四分位数显示未来MR,AR和TR的危险比率显著增加.
- 模型预测与亚临床心室改造相关.
结论:
- 人工智能-心电图模型已成功开发和验证,用于诊断和预测rVHDs.
- 模型在跨国和种族多样化的队列中证明了有效性.
- 人工智能心电图可以指导监控心电图用于早期的rVHD检测和干预.
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