人工智能启用预测单导电心电图的心力衰竭风险
Lovedeep S Dhingra1, Arya Aminorroaya1, Aline F Pedroso1
1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut.
JAMA cardiology
|April 16, 2025
概括
一个人工智能 (AI) 算法可以从单导电心电图 (ECG) 预测心力衰竭 (HF) 风险. 这种AI-ECG模型显示了基于社区的高频风险评估和分层的前景.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗器械 医疗器械
背景情况:
- 需要使用可扩展心力衰竭 (HF) 风险分层策略.
- 便携式单导电心电图 (ECG) 设备为基于社区的风险评估提供了潜力.
研究的目的:
- 评估人工智能 (AI) 算法的预测高频风险的能力.
- 评估AI-ECG在识别左心室缩功能障碍 (LVSD) 和预测新发性HF的表现.
主要方法:
- 一项回顾性和前性队列研究,涉及来自YNHHS,英国生物库和ELSA-巴西的248,000多名参与者.
- 一个适应噪声的AI-ECG模型被训练来识别LVSD从I心电图,模拟可穿戴设备信号.
- 对AI-ECG的表现进行了比较,并将其与已建立的风险评分 (PCP-HF和PREVENT) 进行了比较,以预测新发的HF.
主要成果:
- AI-ECG确定了LVSD,并且与整个队列中HF风险增加3至7倍有关.
- 在AI-ECG模型中,每0.1个增加的概率与27%-65%更高的高频风险相关.
- 与现有的风险评分相比,AI-ECG显示出新发性高频率的优异歧视和重新分类.
结论:
- 适应噪声的AI-ECG模型可以使用I ECG估计HF风险.
- 这种AI-ECG方法为高频风险分层提供了一个潜在的策略.
- 使用可穿戴和便携式心电图设备进行进一步的前性研究是有必要的.
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