通过AI-ECG和基于种族和HCM亚型的差异进行过度缩放性心肌病的高级诊断
Myra Lewontin1, Emily Kaplan1, Kenneth C Bilchick1
1Cardiovascular Division, Department of Medicine, University of Virginia Health System, Charlottesville, VA 22903, USA.
Journal of clinical medicine
|July 12, 2025
概括
人工智能对心电图 (AI-ECG) 的分析可以比传统方法更早地诊断高性心肌病 (HCM). 这项技术有望减少诊断延迟,特别是在服务不足的人群中.
科学领域:
- 心脏病学 心脏病学
- 医学诊断 医学诊断 医学诊断
- 人工智能的人工智能
背景情况:
- 过高心肌病 (HCM) 诊断往往会延迟,导致诊断不足.
- 服务不足的患者群体可能会经历更大的HCM诊断延迟.
- 早期发现HCM对于及时管理和改善患者的治疗结果至关重要.
研究的目的:
- 评估人工智能分析心电图 (AI-ECG) 早期诊断HCM的潜力.
- 测试AI-ECG可以在临床诊断前的回顾性队列中识别HCM的假设.
- 为了评估AI-ECG的诊断时间差异,用于各种患者子组.
主要方法:
- 从404名患者的3499张心电图进行了回顾性分析,这些患者在15年内被转介到HCM卓越中心.
- 盲目应用AI-ECG来预测HCM的概率.
- 计算AI-ECG和临床诊断之间的时间差异,分析灵敏度,特异性和预测值.
主要成果:
- AI-ECG在155名患者中发现了HCM,敏感度为67%,特异性为95% (AUC为0.91).
- 对于一些患者来说,AI-ECG提供了比临床诊断早 16.3 年的诊断.
- 黑人患者比白人患者更有可能接受早期AI-ECG诊断 (p=0.005).
结论:
- 人工智能心电图 (AI-ECG) 显示出有很大的潜力,有助于推进高伤心肌病的诊断.
- 患者子组之间的诊断时间差异突显了现有的医疗保健不平等.
- 人工智能-心电图可能在改善服务不足的族群的早期诊断方面提供最大的好处.
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