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

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 wear...

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Bibliographic Details
Published in:Scientific reports Vol. 10; no. 1; pp. 20495 - 10
Main Authors: Cho, Younghoon, Kwon, Joon-myoung, Kim, Kyung-Hee, Medina-Inojosa, Jose R., Jeon, Ki-Hyun, Cho, Soohyun, Lee, Soo Youn, Park, Jinsik, Oh, Byung-Hee
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 24.11.2020
Nature Publishing Group
Nature Portfolio
Subjects:
639/705/258
692/4019
Algorithms
Angiography
Artificial Intelligence
Coronary Vessels - diagnostic imaging
Coronary Vessels - pathology
Deep Learning
EKG
Electrocardiography
Female
Heart attacks
Humanities and Social Sciences
Humans
Male
Middle Aged
multidisciplinary
Myocardial infarction
Myocardial Infarction - diagnosis
Myocardial Infarction - diagnostic imaging
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-77599-6