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

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 in...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956114/
https://www.ncbi.nlm.nih.gov/pubmed/35330336
http://dx.doi.org/10.3390/jpm12030336
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author 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
author_facet 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
author_sort Choi, Hyun Young
collection PubMed
description 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.
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spelling pubmed-89561142022-03-26 Diagnostic Accuracy of the Deep Learning Model for the Detection of ST Elevation Myocardial Infarction on Electrocardiogram 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 J Pers Med Article 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. MDPI 2022-02-23 /pmc/articles/PMC8956114/ /pubmed/35330336 http://dx.doi.org/10.3390/jpm12030336 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
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
Diagnostic Accuracy of the Deep Learning Model for the Detection of ST Elevation Myocardial Infarction on Electrocardiogram
title Diagnostic Accuracy of the Deep Learning Model for the Detection of ST Elevation Myocardial Infarction on Electrocardiogram
title_full Diagnostic Accuracy of the Deep Learning Model for the Detection of ST Elevation Myocardial Infarction on Electrocardiogram
title_fullStr Diagnostic Accuracy of the Deep Learning Model for the Detection of ST Elevation Myocardial Infarction on Electrocardiogram
title_full_unstemmed Diagnostic Accuracy of the Deep Learning Model for the Detection of ST Elevation Myocardial Infarction on Electrocardiogram
title_short Diagnostic Accuracy of the Deep Learning Model for the Detection of ST Elevation Myocardial Infarction on Electrocardiogram
title_sort diagnostic accuracy of the deep learning model for the detection of st elevation myocardial infarction on electrocardiogram
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956114/
https://www.ncbi.nlm.nih.gov/pubmed/35330336
http://dx.doi.org/10.3390/jpm12030336
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