Smartphone AI vs. Medical Experts: A Comparative Study in Prehospital STEMI Diagnosis.

IF 2.6 4区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Seung Hyo Lee, Won Pyo Hong, Joonghee Kim, Youngjin Cho, Eunkyoung Lee
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引用次数: 0

Abstract

Purpose: Prehospital telecardiology facilitates early ST-elevation myocardial infarction (STEMI) detection, yet its widespread implementation remains challenging. Extracting digital STEMI biomarkers from printed electrocardiograms (ECGs) using phone cameras could offer an affordable and scalable solution. This study assessed the feasibility of this approach with real-world prehospital ECGs.

Materials and methods: Patients suspected of having STEMI by emergency medical technicians (EMTs) were identified from a policy research dataset. A deep learning-based ECG analyzer (QCG™ analyzer) extracted a STEMI biomarker (qSTEMI) from prehospital ECGs. The biomarker was compared to a group of human experts, including five emergency medical service directors (board-certified emergency physicians) and three interventional cardiologists based on their consensus score (number of participants answering "yes" for STEMI). Non-inferiority of the biomarker was tested using a 0.100 margin of difference in sensitivity and specificity.

Results: Among 53 analyzed patients (24 STEMI, 45.3%), the area under the receiver operating characteristic curve of qSTEMI and consensus score were 0.815 (0.691-0.938) and 0.736 (0.594-0.879), respectively (p=0.081). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of qSTEMI were 0.750 (0.583-0.917), 0.862 (0.690-0.966), 0.826 (0.679-0.955), and 0.813 (0.714-0.929), respectively. For the consensus score, sensitivity, specificity, PPV, and NPV were 0.708 (0.500-0.875), 0.793 (0.655-0.966), 0.750 (0.600-0.941), and 0.760 (0.655-0.880), respectively. The 95% confidence interval of sensitivity and specificity differences between qSTEMI and consensus score were 0.042 (-0.099-0.182) and 0.103 (-0.043-0.250), respectively, confirming qSTEMI's non-inferiority.

Conclusion: The digital STEMI biomarker, derived from printed prehospital ECGs, demonstrated non-inferiority to expert consensus, indicating a promising approach for enhancing prehospital telecardiology.

智能手机人工智能与医疗专家:院前 STEMI 诊断比较研究》。
目的:院前远程心电图有助于早期 ST 段抬高型心肌梗死(STEMI)的检测,但其广泛实施仍面临挑战。利用手机摄像头从打印的心电图(ECG)中提取数字 STEMI 生物标志物可提供一种经济实惠且可扩展的解决方案。本研究利用真实的院前心电图评估了这种方法的可行性:从政策研究数据集中识别出急诊医疗技术人员(EMTs)怀疑患有 STEMI 的患者。基于深度学习的心电图分析仪(QCG™ 分析仪)从院前心电图中提取出 STEMI 生物标记物(qSTEMI)。该生物标记物与一组人类专家进行了比较,其中包括五名急诊医疗服务主任(经委员会认证的急诊医师)和三名介入心脏病专家,比较的依据是他们的共识得分(对 STEMI 回答 "是 "的参与者人数)。以 0.100 的灵敏度和特异性差异检验了生物标记物的非劣效性:在 53 名接受分析的患者中(24 名 STEMI,45.3%),qSTEMI 和共识评分的接收者操作特征曲线下面积分别为 0.815(0.691-0.938)和 0.736(0.594-0.879)(P=0.081)。qSTEMI的敏感性、特异性、阳性预测值(PPV)和阴性预测值(NPV)分别为0.750(0.583-0.917)、0.862(0.690-0.966)、0.826(0.679-0.955)和0.813(0.714-0.929)。共识评分的敏感性、特异性、PPV 和 NPV 分别为 0.708(0.500-0.875)、0.793(0.655-0.966)、0.750(0.600-0.941)和 0.760(0.655-0.880)。qSTEMI与共识评分的敏感性和特异性差异的95%置信区间分别为0.042(-0.099-0.182)和0.103(-0.043-0.250),证实了qSTEMI的非劣效性:结论:从打印的院前心电图中提取的数字 STEMI 生物标记物与专家共识相比并无劣势,这表明院前远程心电图是一种很有前途的方法。
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来源期刊
Yonsei Medical Journal
Yonsei Medical Journal 医学-医学:内科
CiteScore
4.50
自引率
0.00%
发文量
167
审稿时长
3 months
期刊介绍: The goal of the Yonsei Medical Journal (YMJ) is to publish high quality manuscripts dedicated to clinical or basic research. Any authors affiliated with an accredited biomedical institution may submit manuscripts of original articles, review articles, case reports, brief communications, and letters to the Editor.
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