Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and cardiovascular outcomes

There is a paucity of data on artificial intelligence-estimated biological electrocardiography (ECG) heart age (AI ECG-heart age) for predicting cardiovascular outcomes, distinct from the chronological age (CA). We developed a deep learning-based algorithm to estimate the AI ECG-heart age using stan...

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Bibliographic Details
Published in:Frontiers in cardiovascular medicine 2023-04, Vol.10, p.1137892-1137892
Main Authors: Baek, Yong-Soo, Lee, Dong-Ho, Jo, Yoonsu, Lee, Sang-Chul, Choi, Wonik, Kim, Dae-Hyeok
Format: Article
Language:English
Subjects:
artificial intelligence
biological ageing
Cardiovascular Medicine
ECG age
heart age
hospitalization
mortality
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Summary:There is a paucity of data on artificial intelligence-estimated biological electrocardiography (ECG) heart age (AI ECG-heart age) for predicting cardiovascular outcomes, distinct from the chronological age (CA). We developed a deep learning-based algorithm to estimate the AI ECG-heart age using standard 12-lead ECGs and evaluated whether it predicted mortality and cardiovascular outcomes. We trained and validated a deep neural network using the raw ECG digital data from 425,051 12-lead ECGs acquired between January 2006 and December 2021. The network performed a holdout test using a separate set of 97,058 ECGs. The deep neural network was trained to estimate the AI ECG-heart age [mean absolute error, 5.8 ± 3.9 years; R-squared, 0.7 (  = 0.84,  
ISSN:2297-055X
2297-055X
DOI:10.3389/fcvm.2023.1137892