ArticleEuropean heart journal. Digital health2023
Correlation between artificial intelligence-enabled electrocardiogram and echocardiographic features in aortic stenosis.
Article in European heart journal. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 13 citations in OpenAlex.
- Rhythm-Stratified Performance of an Artificial Intelligence-Electrocardiographic Aortic Stenosis Score: Alignment with Computed Tomography Calcium in Atrial Fibrillation.Mayo Clinic proceedings. Digital health · 2026Article
- Diastolic dysfunction is linked to the initiation and progression of aortic stenosis: a hypothesis.European heart journal · 2026Review
- Digital phenotyping of aortic stenosis-related remodeling reveals complementary structural, electrical, and hemodynamic signatures.medRxiv : the preprint server for health sciences · 2026Article
- Multi-omics analysis of genetic drivers linking aortic stenosis and left ventricular diastolic dysfunction in heart failure.BioData mining · 2026Article
- Validation and longitudinal trajectory analysis of an AI-based ECG model for aortic stenosis: from community screening to pre-TAVR risk stratification.European heart journal. Digital health · 2026Article
- Prognostic Value of Artificial Intelligence-Enabled Electrocardiography-Derived Diastolic Dysfunction Grading and Trajectory in Patients Undergoing Transcatheter Aortic Valve Replacement.Journal of the American Heart Association · 2026Article
- Artificial Intelligence in Sports Cardiology: Advancing Cardiovascular Screening and Diagnosis.Cureus · 2026Review
- State of the Art of Primary PCI: Present and Future.Journal of clinical medicine · 2025Review
- Artificial intelligence enabled interpretation of ECG images to predict hematopoietic cell transplantation toxicity.Blood advances · 2024Article
- Artificial intelligence-enhanced patient evaluation: bridging art and science.European heart journal · 2024Review
- AI-enabled ECG index for predicting left ventricular dysfunction in patients with ST-segment elevation myocardial infarction.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: An artificial intelligence-enabled electrocardiogram (AI-ECG) is a promising tool to detect patients with aortic stenosis (AS) before developing symptoms. However, functional, structural, or haemodynamic components reflected in AI-ECG responsible for its detection are unknown. Methods and results: The AI-ECG model that was developed at Mayo Clinic using a convolutional neural network to identify patients with moderate-severe AS was applied. In patients used as the testing group, the correlation between the AI-ECG probability of AS and echocardiographic parameters was investigated. This study included 102 926 patients (63.0 ± 16.3 years, 52% male), and 28 464 (27.7%) were identified as AS positive by AI-ECG. Older age, atrial fibrillation, hypertension, diabetes, coronary artery disease, and heart failure were more common in the positive AI-ECG group than in the negative group ( Conclusion: A combination of AS severity, diastolic dysfunction, and LV hypertrophy is reflected in the AI-ECG to detect AS. There seems to be a gradation of the cardiac anatomical/functional features in the model and its identification process of AS is multifactorial.
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