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, CT, USA.
medRxiv : the preprint server for health sciences
|June 10, 2024
概括
使用单导电心电图 (ECG) 的人工智能 (AI) 算法有效预测心力衰竭 (HF) 风险. 这种AI-ECG方法为基于社区的高频风险评估提供了一个可扩展的策略.
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:
- 尽管有可用的治疗方法,可扩展心力衰竭 (HF) 风险分层仍然是一个挑战.
- 便携式单导电心电图 (ECG) 设备为大规模风险评估提供了潜力.
研究的目的:
- 评估一种人工智能 (AI) 算法,用于使用杂的单导电心电图来预测高频风险.
- 评估AI-ECG在识别有左心室缩功能障碍 (LVSD) 风险的个体方面的表现.
主要方法:
- 一项多队列研究使用了回顾性 (耶鲁纽黑文卫生系统) 和前性 (英国生物银行,ELSA-巴西) 数据.
- 一个适应噪声的AI-ECG模型被应用到I ECG以识别LVSD和预测新发性HF.
- 使用歧视指标,AI-ECG的表现与已确定的风险得分 (PCP-HF,PREVENT) 进行了比较.
主要成果:
- 人工智能电脑电图 (AI-ECG) 确定了3至7倍增加HF风险的个体,更高的模型概率与增加的危险相关.
- 在AI-ECG中,跨队列的新发性HF受到强烈的歧视 (C统计 0.725-0.833).
- 与PCP-HF和PREVENT方程相比,纳入AI-ECG可以改善风险预测,显示C统计,IDI和NRI的显著改善.
结论:
- 使用I心电图的适应噪声的人工智能模型可以有效地分层心力衰竭风险.
- 这种AI-ECG方法表明,基于便携式和可穿戴设备的高频风险分层是一个有希望的策略.
- 这些发现支持使用可访问的ECG技术进行广泛的基于社区的高频风险评估的潜力.
相关概念视频
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An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
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An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
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