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用于检测心电图上ST段抬高型心肌梗死的深度学习模型的诊断准确性

Diagnostic Accuracy of the Deep Learning Model for the Detection of ST Elevation Myocardial Infarction on Electrocardiogram.

作者信息

Choi Hyun Young, Kim Wonhee, Kang Gu Hyun, Jang Yong Soo, Lee Yoonje, Kim Jae Guk, Lee Namho, Shin Dong Geum, Bae Woong, Song Youngjae

机构信息

Department of Emergency Medicine, College of Medicine, Hallym University, Chuncheon 24252, Korea.

Division of Cardiology, Department of Internal Medicine, College of Medicine, Hallym University, Chuncheon 24252, Korea.

出版信息

J Pers Med. 2022 Feb 23;12(3):336. doi: 10.3390/jpm12030336.

DOI:10.3390/jpm12030336
PMID:35330336
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8956114/
Abstract

We aimed to measure the diagnostic accuracy of the deep learning model (DLM) for ST-elevation myocardial infarction (STEMI) on a 12-lead electrocardiogram (ECG) according to culprit artery sorts. From January 2017 to December 2019, we recruited patients with STEMI who received more than one stent insertion for culprit artery occlusion. The DLM was trained with STEMI and normal sinus rhythm ECG for external validation. The primary outcome was the diagnostic accuracy of DLM for STEMI according to the three different culprit arteries. The outcomes were measured using the area under the receiver operating characteristic curve (AUROC), sensitivity (SEN), and specificity (SPE) using the Youden index. A total of 60,157 ECGs were obtained. These included 117 STEMI-ECGs and 60,040 normal sinus rhythm ECGs. When using DLM, the AUROC for overall STEMI was 0.998 (0.996-0.999) with SEN 97.4% (95.7-100) and SPE 99.2% (98.1-99.4). There were no significant differences in diagnostic accuracy within the three culprit arteries. The baseline wanders in false positive cases (83.7%, 345/412) significantly interfered with the accurate interpretation of ST elevation on an ECG. DLM showed high diagnostic accuracy for STEMI detection, regardless of the type of culprit artery. The baseline wanders of the ECGs could affect the misinterpretation of DLM.

摘要

我们旨在根据罪犯血管分类,测量深度学习模型(DLM)对12导联心电图(ECG)上ST段抬高型心肌梗死(STEMI)的诊断准确性。2017年1月至2019年12月,我们招募了因罪犯血管闭塞接受不止一次支架置入的STEMI患者。DLM使用STEMI和正常窦性心律心电图进行训练以进行外部验证。主要结局是DLM根据三种不同罪犯血管对STEMI的诊断准确性。使用受试者工作特征曲线下面积(AUROC)、敏感度(SEN)和特异度(SPE)并采用约登指数来衡量结局。共获得60157份心电图。其中包括117份STEMI心电图和60040份正常窦性心律心电图。使用DLM时,总体STEMI的AUROC为0.998(0.996 - 0.999),SEN为97.4%(95.7 - 100),SPE为99.2%(98.1 - 99.4)。三种罪犯血管内的诊断准确性无显著差异。假阳性病例中的基线漂移(83.7%,345/412)显著干扰了心电图上ST段抬高的准确判读。无论罪犯血管类型如何,DLM对STEMI检测均显示出高诊断准确性。心电图的基线漂移可能会影响DLM的误判。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e3e/8956114/7eca58a817ac/jpm-12-00336-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e3e/8956114/9aedc8420658/jpm-12-00336-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e3e/8956114/bc1cc2439898/jpm-12-00336-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e3e/8956114/7eca58a817ac/jpm-12-00336-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e3e/8956114/9aedc8420658/jpm-12-00336-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e3e/8956114/bc1cc2439898/jpm-12-00336-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e3e/8956114/7eca58a817ac/jpm-12-00336-g003.jpg

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Lipids Health Dis. 2021 May 6;20(1):48. doi: 10.1186/s12944-021-01475-z.
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3
Comparison of the ST-Elevation Myocardial Infarction (STEMI) vs. NSTEMI and Occlusion MI (OMI) vs. NOMI Paradigms of Acute MI.
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J Emerg Med. 2021 Mar;60(3):273-284. doi: 10.1016/j.jemermed.2020.10.026. Epub 2020 Dec 9.
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Time for clinicians to revisit their perspectives on C-statistic.临床医生是时候重新审视他们对C统计量的看法了。
Eur Heart J. 2021 Jan 1;42(1):132-133. doi: 10.1093/eurheartj/ehaa859.
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