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智能手机人工智能与医学专家:在医院前的STEMI诊断中的比较研究

Seung Hyo Lee¹, Won Pyo Hong^2,3, Joonghee Kim^4,5

¹National Fire Agency Pre-hospital Emergency Medical Research TF, Sejong, Korea.

Yonsei medical journal

|February 19, 2024

概括

从打印的心电图的手机摄像头图像中提取的新的数字生物标志物 (qSTEMI) 对检测ST升高心肌梗塞 (STEMI) 的专家共识不逊色. 这为医院前心电心脏病学提供了一个可扩展的解决方案.

科学领域:

心脏病学心脏病学
医疗技术医疗技术医学技术
人工智能的人工智能

背景情况:

医院前心电心脏学有助于早期检测ST升高心肌梗塞 (STEMI),但面临实施挑战.
利用手机摄像头从打印的心电图中提取数字STEMI生物标志物,提供了一个负担得起和可扩展的替代方案.

研究的目的:

评估使用手机摄像头从医院前心电图中提取数字STEMI生物标志物的可行性.
将这个数字生物标志物的性能与人类专家的共识进行比较.

主要方法:

一个深度学习分析仪 (QCGTM分析仪) 从被怀疑患有STEMI的患者的医院前心电图中提取了STEMI生物标志物 (qSTEMI).
将qSTEMI生物标志物与急诊医生和干预心脏病学家的共识得分进行了比较.
使用0.100的灵敏度和特异性差额进行了非劣质性测试.

主要成果:

qSTEMI生物标志物在ROC曲线下的面积达到0.815,而专家共识的面积为0.736 (p=0.081).
qSTEMI表现出0.750的灵敏度和0.862的特异性,具有与专家共识相比较的正负预测值.
在敏感性和特异性方面,qSTEMI与专家共识的非劣势得到证实.

结论:

从印刷的医院前心电图中获得的数字STEMI生物标志物显示,与专家共识相比,其性能不逊色.
这种方法有望提高STEMI检测医院前心电术的可扩展性和可负担性.

关键词:

这是一个ECGECGECGECGECG.在医院前的急救护理.这就是为什么我们会用声音来表达.深度学习是一种深度学习.紧急医疗服务紧急医疗服务

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