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Cited by 224ECG Monitoring and AnalysisPhonocardiography and Auscultation TechniquesArtificial Intelligence in Healthcare

Deep Learning–Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography

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Kwon, Joon‐Myoung, Lee, Soo Youn, Jeon, Ki‐Hyun, Lee, Yeha, Kim, Kyung‐Hee, Park, Jinsik, Oh, Byung‐Hee, Lee, Myong‐Mook·2020·Journal of the American Heart Association
DOI 10.1161/jaha.119.014717PMC PMC7428650
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Abstract

Background: Severe, symptomatic aortic stenosis ( AS ) is associated with poor prognoses. However, early detection of AS is difficult because of the long asymptomatic period experienced by many patients, during which screening tools are ineffective. The aim of this study was to develop and validate a deep learning–based algorithm, combining a multilayer perceptron and convolutional neural network, for detecting significant AS using ECGs. Methods and Results: This retrospective cohort study included adult patients who had undergone both ECG and echocardiography. A deep learning–based algorithm was developed using 39 371 ECG s. Internal validation of the algorithm was performed with 6453 ECG s from one hospital, and external validation was performed with 10 865 ECG s from another hospital. The end point was significant AS (beyond moderate). We used demographic information, features, and 500‐Hz, 12‐lead ECG raw data as predictive variables. In addition, we identified which region had the most significant effect on the decision‐making of the algorithm using a sensitivity map. During internal and external validation, the areas under the receiver operating characteristic curve of the deep learning–based algorithm using 12‐lead ECG for detecting significant AS were 0.884 (95% CI, 0.880–0.887) and 0.861 (95% CI, 0.858–0.863), respectively; those using a single‐lead ECG signal were 0.845 (95% CI, 0.841–0.848) and 0.821 (95% CI, 0.816–0.825), respectively. The sensitivity map showed the algorithm focused on the T wave of the precordial lead to determine the presence of significant AS . Conclusions: The deep learning–based algorithm demonstrated high accuracy for significant AS detection using both 12‐lead and single‐lead ECG s.

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  • Valvular and composite SHD AI-ECG models are promising for referral enrichment.

    “Valvular and composite SHD models are promising for referral enrichment”

    From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [20]

    The Methods (Statistical Analysis; page unavailable) describe the AI-ECG algorithm as a screen for aortic stenosis before confirmatory echocardiography, supporting its potential to help select patients for referral. The paper studies aortic stenosis, however, and does not support the claim about composite structural heart disease (SHD) models. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Kwon et al. independently reported AUCs of 0.884 in internal validation and 0.861 in external validation using a combined multilayer perceptron-convolutional neural network architecture.

    “Kwon et al independently reported AUCs of 0.884 in internal validation and 0.861 in external validation using a combined multilayer perceptron–convolutional neural network architecture.20”

    From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [20]

  • Valvular disease may produce electrical signatures detectable beyond standard ECG criteria.

    “Kwon et al. developed a deep learning algorithm to detect aortic stenosis from ECGs, demonstrating that valvular disease may produce electrical signatures detectable beyond standard ECG criteria [29].”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [29]

  • A deep learning algorithm detected aortic stenosis from ECGs.

    “Kwon et al. developed a deep learning algorithm to detect aortic stenosis from ECGs, demonstrating that valvular disease may produce electrical signatures detectable beyond standard ECG criteria [29].”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [29]

  • Deep learning has been shown to be beneficial in detecting specific heart diseases from the 12-lead ECG, including aortic stenosis, low LVEF, low left ventricular hypertrophy and composites of valvular diseases.

    “The application of deep learning, a subset of artificial intelligence (AI), has been shown to be beneficial in the detection of specific heart diseases from the 12-lead electrocardiogram (ECG). This includes conditions such as aortic stenosis, low left ventricular ejection fraction (LVEF) and low left ventricular hypertrophy, as well as composites of many valvular diseases3,17–21.”

    From Detecting structural heart disease from electrocardiograms using AI (2025) · cites this paper as [18]

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Cited by

Sorted by citations

Authors
Kwon, J., Lee, S. Y., Jeon, K., Lee, Y., Kim, K., Park, J., Oh, B., & Lee, M.
Affiliations
  • Sejong Institute (KR)
  • Sejong General Hospital (KR)
  • VUNO Seoul Korea
  • Division of Cardiology Cardiovascular Center Incheon Korea
Corresponding author
Lee, Soo Youn
ORCID
Venue
Journal of the American Heart Association
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2020-04-09
Volume
9
Issue
7
Pages
e014717-e014717
ISSN
2047-9980 (ISSN-L)
DOI
10.1161/jaha.119.014717
PMC
PMC7428650
OpenAlex
W3012755169
Cited by
224
References
6
Citations by year
2026: 23 · 2025: 45 · 2024: 39 · 2023: 36 · 2022: 42 · 2021: 29 · 2020: 10
FWCI
18.09
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Journal of the American Heart Association (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
aortic stenosis, electrocardiogram, deep learning, convolutional neural networks, multilayer perceptron, 12-lead ECG, single-lead ECG, early detection, echocardiography, sensitivity maps, T wave, algorithm validation
MeSH terms
Deep Learning, Aged, Aged, 80 and over, Aortic Valve, Aortic Valve Stenosis, Electrocardiography, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Retrospective Studies, Severity of Illness Index, Signal Processing, Computer-Assisted, Reproducibility of Results, Early Diagnosis, Republic of Korea

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