ArticleThe Korean journal of internal medicine2026
Enhanced prediction of left ventricular ejection fraction using electrocardiography with the addition of clinical metadata.
Article in The Korean journal of internal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Artificial intelligence-enabled electrocardiography for assessment of left ventricular systolic dysfunction in the era of foundation models.Frontiers in cardiovascular medicine · 2026Review
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4 authors.
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Abstract
BACKGROUND/
aimsLeft ventricular ejection fraction (LVEF) is a key echocardiographic parameter for assessing LV systolic function, guiding the management of many cardiovascular diseases, including heart failure (HF). While traditional electrocardiography (ECG) has been widely used in clinical practice, it has limitations in predicting LVEF. This study investigated the impact of integrating ECG data with metadata, such as age, N-terminal pro B-type natriuretic peptide (NT-proBNP), and sodium levels, to enhance the accuracy of LVEF prediction, especially in HF with reduced ejection fraction (HFrEF, LVEF ≤ 40%).
methodsThis retrospective study analyzed ECG and metadata from two tertiary teaching hospitals in Korea. A deep neural network (EfficientNet B3) was trained to predict LVEF, incorporating clinical metadata alongside ECG inputs. Model performance was assessed using the area under the curve (AUC) and the coefficient of determination (R2).
resultsThe artificial intelligence (AI) model achieved an AUC of 0.95 when ECG data were combined with age, NT-proBNP, and sodium levels, outperforming models relying on ECG alone (AUC = 0.90). The integration of metadata significantly improved the prediction accuracy, particularly for HFrEF cases. The specificity of the model remained high (96.9%), but sensitivity was relatively low (54.8%), indicating its potential as a screening tool for HFrEF.
conclusionThe combination of ECG and metadata results using AI enhances the predictive accuracy of HFrEF detection. This approach offers a scalable and noninvasive method for HF screening and risk stratification, particularly in resource-limited settings. Further validation in diverse populations is needed to confirm its clinical utility.
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