人工智能用于预测心房动的跨民族验证,使用鼻节律心电图
Ji Hyun Lee1, Joonghee Kim2,3, Jina Choi1
1Cardiovascular Center, Department of Internal Medicine.
Journal of cardiovascular medicine (Hagerstown, Md.)
|December 22, 2025
概括
一个人工智能模型有效地使用标准心电图 (ECG) 图像预测心房动. 这种人工智能工具在不同种族群体中表现出强的表现,表明了早期检测的广泛临床潜力.
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:
- 心房动 (AF) 是一种常见的心律失常症,需要及早检测.
- 标准的12导管鼻腔节律心电图 (ECG) 是广泛可用的,但对AF风险进行解释可能具有挑战性.
研究的目的:
- 开发和评估一个人工智能 (AI) 模型,从标准的12导突节律心电图预测心房动 (AF).
- 验证人工智能模型在不同族裔群体,特别是韩国和巴西人群中的表现.
主要方法:
- 一个修改的卷积神经网络 (CNN) 使用来自121,600名韩国患者的811,542个ECG的大数据集被开发出来.
- 内部验证使用了10%的韩国患者数据;外部验证使用了来自巴西的CODE 15%数据集.
- 对巴西验证数据集 (AF:非AF) 应用了1:4的随机抽样策略.
主要成果:
- 在内部验证中,AI模型在接收器操作特性曲线 (AUROC) 下的面积为0.907.
- 巴西队伍的外部验证显示AUROC为0.884,在年龄和性别调整后增加到0.906.
- 对于"正常的心电图"解释,AUROC为0.826,经过调整后改善到0.861.
结论:
- 由人工智能驱动的心电图解释模型表现出极好的预测性能,用于心脏动或发生性心房动.
- 该模型在巴西人口中得到验证的表现表明,该模型在不同种族群体中具有广泛应用的潜力.
- 这种人工智能工具有望改善心房的早期检测和管理.
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