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ECG-Based Artificial Intelligence for Classifying Left Ventricular Dysfunction and Heart Failure With Preserved
Ibrahim Karabayir1, Olivia Gilbert1, Ali Valika2
1Department of Cardiovascular Medicine Wake Forest School of Medicine Winston-Salem NC USA.
Journal of the American Heart Association
|July 28, 2026
Summary
An artificial intelligence (AI) tool using electrocardiogram (ECG) data can accurately detect left ventricular (LV) dysfunction and heart failure with preserved ejection fraction (HFpEF). This ECG-AI tool shows potential for widespread, low-cost cardiovascular screening.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Left ventricular (LV) dysfunction and heart failure with preserved ejection fraction (HFpEF) often present with subtle early signs.
- These early indicators are frequently misattributed to other conditions, delaying diagnosis and treatment.
Purpose of the Study:
- To develop and validate a novel artificial intelligence (AI) tool utilizing electrocardiogram (ECG) data for the simultaneous detection of LV dysfunction and HFpEF subtypes.
- To assess the efficacy of AI models trained on both 12-lead and single-lead ECG data.
Main Methods:
- Developed two ECG-AI models (12-lead and single-lead ECG) using data from Atrium Health Wake Forest Baptist and UTHSC.
- Classified ECGs into four categories: reduced ejection fraction (rEF), midrange EF (mEF), HFpEF, and controls.
- Validated models on independent adult and pediatric datasets, incorporating clinical risk factors and comparing ECG-AI with clinical data-only models.
Main Results:
- The 12-lead ECG-AI model demonstrated high accuracy, with areas under the curve (AUC) for rEF/mEF/HFpEF reaching 0.90/0.81/0.80 in the primary cohort and 0.92/0.76/0.73 in the external validation cohort.
- The single-lead ECG-AI model also showed strong performance, with AUCs of 0.89/0.78/0.75 and 0.90/0.75/0.74, respectively.
- ECG-AI models outperformed clinical data-only models, and the addition of clinical data did not significantly improve ECG-AI model performance. Pediatric validation showed AUCs of 0.97/0.71/0.64 for 12-lead and 0.94/0.77/0.67 for single-lead.
Conclusions:
- ECG-AI is a powerful tool for accurately detecting LV dysfunction and HFpEF.
- The AI tool is effective even when using single-lead ECGs, suggesting potential for low-cost, accessible cardiovascular screening.
- External validation, including on pediatric data, confirms the generalizability and robustness of the ECG-AI approach.
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