AI-ECG detection of diastolic dysfunction: A screening approach for early HFpEF
M Soh, Y Lee, H Hong, S Kang, H Lim, T Yu, Y Na, H An, S Eom, Y Ji, S Joo, J Park, J ShinAbstract
Background
Left ventricular diastolic dysfunction (LVDD) is highly prevalent in the general population and represents an early stage of heart failure with preserved ejection fraction (HFpEF). Diagnosis relies on Doppler-based echocardiographic parameters, limiting its feasibility for large-scale screening. While artificial intelligence (AI)–enabled electrocardiography has shown strong performance for detecting systolic dysfunction, ECG-based detection of LVDD remains less explored. We evaluated whether a deep learning model could identify guideline-defined LVDD using 12-lead ECGs.
Methods
We retrospectively analyzed paired ECG and echocardiographic examinations obtained between 2020 and 2023 at a tertiary academic medical center. LVDD was defined according to the 2016 ASE/EACVI recommendations using four parameters: mean E/e′ ratio (>14), septal e′ velocity (<0.07 m/s) or lateral e′ velocity (<0.10 m/s), tricuspid regurgitation velocity (>2.8 m/s), and left atrial volume index (>34 mL/m²). Patients meeting more than half of the evaluable criteria were classified as LVDD. ECG–echocardiography pairs were matched within ±14 days. A Vision Transformer pretrained on unlabeled ECGs was fine-tuned to classify LVDD. Data were split at the patient level into training, validation, and test sets (7:1:2). After exclusions, 46,564 ECG–echocardiography pairs from 21,385 patients were included.
Results
LVDD prevalence in the test cohort was 15.3%. The model achieved an AUROC of 0.908 (95% CI 0.893–0.922) in the validation set and 0.913 (95% CI 0.903–0.924) in the test set. Using a threshold defined in the validation cohort where sensitivity equaled specificity, the model demonstrated sensitivity 0.838, specificity 0.825, and overall accuracy 0.827. The positive predictive value was 0.463, whereas the negative predictive value reached 0.966, supporting potential utility as a rule-out screening tool.
Conclusion
A deep learning model using 12-lead ECGs demonstrated strong discrimination for detecting LVDD with an AUROC exceeding 0.90. ECG-based AI screening may provide a scalable and non-invasive strategy for early identification of diastolic dysfunction and facilitate earlier intervention before progression to symptomatic HFpEF.Performance of AI-ECG model for LVDDROC and curve for LVDD prediction