电脑心电图像分析软件的跨民族验证,用于检测急诊室人口的左心室功能障碍
Haemin Lee1,2, Woon Yong Kwon3, Kyoung Jun Song4
1Department of Emergency Medicine, Seoul National University Bundang Hospital, 166 Gumi-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea.
Clinical and experimental emergency medicine
|April 30, 2025
概括
一个人工智能工具ECG Buddy准确地使用心电图 (ECG) 预测左心室 (LV) 功能障碍. 它在美国的一项研究中表现优于NT-ProBNP,在识别低射出分数方面表现出高灵敏度和特异性.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 生物标志物发现发现
背景情况:
- 左心室 (LV) 缩功能障碍是心脏健康的关键指标.
- 准确及时预测LV功能障碍对于患者管理至关重要.
- 现有的生物标志物,如N-终端Pro-B型天然尿素 (NT-ProBNP),在预测LV射出分数 (LVEF) 方面存在局限性.
研究的目的:
- 为了验证基于AI的ECG分析工具ECGBuddy在美国人口中的性能.
- 评估ECG Buddy的LV功能障碍评分和LVEF-ECG功能,以预测LVEF<40%.
- 为了比较ECG Buddy与NT-ProBNP的诊断准确度.
主要方法:
- 使用了MIMIC-IV数据集,包括急诊室 (ED) 访问与LVEF数据并匹配了12心电图.
- 分析了22599次ED访问,将ECG Buddy的LV功能障碍评分和LVEF-ECG特征与NT-ProBNP进行了比较.
- 用户接收器操作特征 - 曲线下的面积 (ROC-AUC) 分析以评估诊断性能.
主要成果:
- ECG Buddy的LV功能障碍得分达到0.905的AUC (灵敏度为85.4%,特异性为80.8%).
- 在LVEF-ECG特征中,AUC值为0.908 (灵敏度为83.5%,特异性为83.0%).
- 这两种基于心电图的预测指标在预测LVEF<40% (p<0.001) 时显著超过NT-ProBNP (AUC0.740).
结论:
- 与NT-ProBNP相比,ECG Buddy在预测LV缩功能障碍方面显示出更高的准确性.
- 基于人工智能的心电图分析工具已被验证用于美国急诊室的人群.
- ECG Buddy为评估LVEF和指导临床决策提供了一种有前途的非侵入性方法.
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