一个人工智能启用的心电图,用于评估急诊室的呼吸障碍患者
Hee Tae Yu1,2, Laura E Walker3, Eunjung Lee1
1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
Mayo Clinic proceedings. Innovations, quality & outcomes
|October 24, 2025
概括
人工智能支持的心电图 (AI-ECG) 可以准确地评估急诊室患者的舒张功能和填充压力. 这种AI-ECG工具可以有效地区分心脏和非心脏性呼吸障碍的原因,有助于及时诊断.
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:
- 不分化的呼吸障碍是一种常见的急诊室表现.
- 及时识别呼吸障碍的心脏原因对于患者管理至关重要.
- 升高的左心室填充压力往往表明心脏呼吸障碍.
研究的目的:
- 评估人工智能启用心电图 (AI-ECG) 在确定腹功能和填充压力的有效性.
- 评估AI-ECG是否可以在急诊室患者中区分心脏和非心脏性呼吸障碍的起源.
主要方法:
- 分析了在2020年1月至2022年12月期间出现ED呼吸障碍的2412名成年患者的队列.
- 使用AI-ECG工具评估左心室透静功能并估计填充压力.
- 人工智能-心电图的发现与随后的评估确定的最终诊断相关.
主要成果:
- 在2412名患者中,966名 (40%) 患有心脏呼吸障碍,1446名 (60%) 患有非心脏呼吸障碍.
- 高度的AI-ECG估计的腹功能障碍 (级别2和3) 与心脏呼吸障碍的更高概率显著相关.
- 心脏呼吸障碍的发生率与AI-ECG估计的填充压力的增加成比例增加.
结论:
- 通过人工智能增强的12导电心电图,可以精确评估透缩功能和填充压力.
- 透气功能的AI-ECG评估表明,它有很强的能力区分ED患者呼吸障碍的心脏原因.
- 这项技术有望提高诊断准确性和急诊室呼吸障碍评估的效率.
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