一个心电图基础模型建立在超过1000万条记录的基础上
Jun Li1,2,3, Aaron D Aguirre4,5, Valdery Moura5,6
1National Institute of Health Data Science, Peking University, Beijing.
NEJM AI
|August 7, 2025
概括
一个新的人工智能 (AI) 基础模型ECGFounder分析心电图 (ECG) 来诊断心血管疾病. 它实现了专家级别的性能,即使在单线心电图上也是如此,从而推进了远程患者监控.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 人工智能 (AI) 在心电图 (ECG) 分析心血管疾病方面表现有前途.
- 基金会模型增强了医疗人工智能,改善了诊断和知识转移.
- 电脑心电图人工智能面临的挑战包括有限的数据和不良的概括性,特别是在单线心电图中.
研究的目的:
- 开发一个通用的ECG基础模型 (ECGFounder),用于全面的心血管疾病诊断.
- 创建一个有效的,即时准备的模型,并且可以轻松微调用于各种下游任务.
- 将ECG分析能力扩展到减少的ECG,包括用于移动和远程监控的单ECG.
主要方法:
- 开发了ECGFounder,使用了来自1,818,247名受试者的10,771,552个ECG,并使用了150个诊断标签.
- 利用心脏病专家的真实世界心电图注释,提供广泛的诊断能力.
- 扩展模型适用于用于多种应用的单ECG.
主要成果:
- 在内部验证方面,ECGFounder取得了专家级别的表现 (AUROC>0.95对80个诊断).
- 在外部验证集上的诊断中展示了强大的分类和概括.
- 在人口统计分析,事件检测和微调后的交叉模式诊断方面表现比基线模型优于3-5个AUROC点.
结论:
- 电动心电图基础模型有效地将任务泛化,增强心血管诊断.
- 便于与云系统集成,用于可穿戴式心电图数据分析.
- 在心脏病学领域显著推进人工智能,并有助于心脏病状况管理.
相关概念视频
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Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
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