用心电图进行无症状COVID-19感染的快速通量查:一项前性观察研究
Demilade Adedinsewo1, Jennifer Dugan2, Patrick W Johnson3
1Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL.
Mayo Clinic proceedings. Digital health
|April 10, 2025
概括
护理点的AI-ECG设备在检测无症状的SARS-CoV-2感染方面无效. 这凸显了在开发AI-ECG疾病检测工具时需要严格的前性测试和类似数据的需要.
科学领域:
- 人工智能在医学中的应用
- 心脏病学 心脏病学
- 传染性疾病 传染性疾病
背景情况:
- 严重急性呼吸系统综合征冠状病毒2 (SARS-CoV-2) 感染构成了全球健康挑战.
- 临床诊断工具对于快速检测至关重要.
- 应用于心电图 (ECG) 的人工智能 (AI) 显示了疾病识别的潜力.
研究的目的:
- 评估神经网络驱动的POCAI-ECG设备用于识别SARS-CoV-2感染的有效性.
- 评估手持式智能手机兼容AI-ECG在检测无症状SARS-CoV-2时的准确性.
主要方法:
- 一项前性观察性研究在2020年12月至2021年6月期间招募了2827名患者.
- 一个经过修改的深度学习模型,在12-lead ECG数据上进行训练,被调整为POC AI-ECG.
- 分析了AI-ECG检测无症状SARS-CoV-2感染的能力.
主要成果:
- POC AI-ECG算法在检测无症状的SARS-CoV-2感染 (AUC = 0.56) 中表现为无效.
- 该算法无法可靠地区分SARS-CoV-2阳性和阴性参与者的心电图.
- 参与者的人口统计数据包括48%的女性,79%的白人和7%的先前COVID-19感染者.
结论:
- 一个POCAI-ECG工具对于在成年人中识别无症状SARS-CoV-2感染是不可靠的.
- 使用类似人群和数据进行前性测试对于开发有效的AI-ECG诊断工具至关重要.
- 这些发现与以前使用12导电图数据的研究形成鲜明对比.
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