Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

Abstract Rapid diagnosis of myocardial infarction (MI) using electrocardiography (ECG) is the cornerstone of effective treatment and prevention of mortality; however, conventional interpretation methods has low reliability for detecting MI and is difficulty to apply to limb 6-lead ECG based life typ...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Younghoon Cho, Joon-myoung Kwon, Kyung-Hee Kim, Jose R. Medina-Inojosa, Ki-Hyun Jeon, Soohyun Cho, Soo Youn Lee, Jinsik Park, Byung-Hee Oh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9929d36a5ebe44cd8f13f0d280c39c94
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:9929d36a5ebe44cd8f13f0d280c39c94
record_format	dspace
spelling	oai:doaj.org-article:9929d36a5ebe44cd8f13f0d280c39c942021-12-02T12:34:18ZArtificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography10.1038/s41598-020-77599-62045-2322https://doaj.org/article/9929d36a5ebe44cd8f13f0d280c39c942020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77599-6https://doaj.org/toc/2045-2322Abstract Rapid diagnosis of myocardial infarction (MI) using electrocardiography (ECG) is the cornerstone of effective treatment and prevention of mortality; however, conventional interpretation methods has low reliability for detecting MI and is difficulty to apply to limb 6-lead ECG based life type or wearable devices. We developed and validated a deep learning-based artificial intelligence algorithm (DLA) for detecting MI using 6-lead ECG. A total of 412,461 ECGs were used to develop a variational autoencoder (VAE) that reconstructed precordial 6-lead ECG using limb 6-lead ECG. Data from 9536, 1301, and 1768 ECGs of adult patients who underwent coronary angiography within 24 h from each ECG were used for development, internal and external validation, respectively. During internal and external validation, the area under the receiver operating characteristic curves of the DLA with VAE using a 6-lead ECG were 0.880 and 0.854, respectively, and the performances were preserved by the territory of the coronary lesion. Our DLA successfully detected MI using a 12-lead ECG or a 6-lead ECG. The results indicate that MI could be detected not only with a conventional 12 lead ECG but also with a life type 6-lead ECG device that employs our DLA.Younghoon ChoJoon-myoung KwonKyung-Hee KimJose R. Medina-InojosaKi-Hyun JeonSoohyun ChoSoo Youn LeeJinsik ParkByung-Hee OhNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Younghoon Cho Joon-myoung Kwon Kyung-Hee Kim Jose R. Medina-Inojosa Ki-Hyun Jeon Soohyun Cho Soo Youn Lee Jinsik Park Byung-Hee Oh Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography
description	Abstract Rapid diagnosis of myocardial infarction (MI) using electrocardiography (ECG) is the cornerstone of effective treatment and prevention of mortality; however, conventional interpretation methods has low reliability for detecting MI and is difficulty to apply to limb 6-lead ECG based life type or wearable devices. We developed and validated a deep learning-based artificial intelligence algorithm (DLA) for detecting MI using 6-lead ECG. A total of 412,461 ECGs were used to develop a variational autoencoder (VAE) that reconstructed precordial 6-lead ECG using limb 6-lead ECG. Data from 9536, 1301, and 1768 ECGs of adult patients who underwent coronary angiography within 24 h from each ECG were used for development, internal and external validation, respectively. During internal and external validation, the area under the receiver operating characteristic curves of the DLA with VAE using a 6-lead ECG were 0.880 and 0.854, respectively, and the performances were preserved by the territory of the coronary lesion. Our DLA successfully detected MI using a 12-lead ECG or a 6-lead ECG. The results indicate that MI could be detected not only with a conventional 12 lead ECG but also with a life type 6-lead ECG device that employs our DLA.
format	article
author	Younghoon Cho Joon-myoung Kwon Kyung-Hee Kim Jose R. Medina-Inojosa Ki-Hyun Jeon Soohyun Cho Soo Youn Lee Jinsik Park Byung-Hee Oh
author_facet	Younghoon Cho Joon-myoung Kwon Kyung-Hee Kim Jose R. Medina-Inojosa Ki-Hyun Jeon Soohyun Cho Soo Youn Lee Jinsik Park Byung-Hee Oh
author_sort	Younghoon Cho
title	Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography
title_short	Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography
title_full	Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography
title_fullStr	Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography
title_full_unstemmed	Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography
title_sort	artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/9929d36a5ebe44cd8f13f0d280c39c94
work_keys_str_mv	AT younghooncho artificialintelligencealgorithmfordetectingmyocardialinfarctionusingsixleadelectrocardiography AT joonmyoungkwon artificialintelligencealgorithmfordetectingmyocardialinfarctionusingsixleadelectrocardiography AT kyungheekim artificialintelligencealgorithmfordetectingmyocardialinfarctionusingsixleadelectrocardiography AT josermedinainojosa artificialintelligencealgorithmfordetectingmyocardialinfarctionusingsixleadelectrocardiography AT kihyunjeon artificialintelligencealgorithmfordetectingmyocardialinfarctionusingsixleadelectrocardiography AT soohyuncho artificialintelligencealgorithmfordetectingmyocardialinfarctionusingsixleadelectrocardiography AT sooyounlee artificialintelligencealgorithmfordetectingmyocardialinfarctionusingsixleadelectrocardiography AT jinsikpark artificialintelligencealgorithmfordetectingmyocardialinfarctionusingsixleadelectrocardiography AT byungheeoh artificialintelligencealgorithmfordetectingmyocardialinfarctionusingsixleadelectrocardiography
_version_	1718393875000721408

Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

Ejemplares similares