ArticleClinical and experimental emergency medicine2025
Interethnic validation of electrocardiogram image analysis software for detecting left ventricular dysfunction in an emergency department population.
Article in Clinical and experimental emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Diagnostic Accuracy of Artificial Intelligence-Based Electrocardiography for the Detection of Heart Diseases: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Review
- Emerging Artificial Intelligence Tools for the Screening of Structural and Valvular Heart Disease.Current heart failure reports · 2026Review
- External validation of ECG artificial intelligence for emergency and cardiac assessment across a large-scale U.S. healthcare system.NPJ digital medicine · 2026Article
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Authors and funding
8 authors.
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Abstract
objectiveWe previously developed and validated an artificial intelligence-based electrocardiogram (ECG) analysis tool (ECG Buddy) in a Korean population. This study investigated the performance of this tool in a US population, specifically assessing the left ventricular (LV) dysfunction score and LV ejection fraction (LVEF)-ECG feature for predicting LVEF <40%. The study used N-terminal pro-B-type natriuretic peptide (NT-ProBNP) as a comparator.
methodsWe identified emergency department (ED) visits from the MIMIC-IV dataset with information on LVEF <40% or ≥40% and matched 12-lead ECG data recorded within 48 hours of the ED visit. The performance of ECG Buddy's LV dysfunction score and the LVEF-ECG feature was compared with those of NT-ProBNP using area under the receiver operating characteristic curve (AUC) analysis.
resultsA total of 22,599 ED visits was analyzed. The LV dysfunction score had an AUC of 0.905 (95% confidence interval [CI], 0.899-0.910), with a sensitivity of 85.4% and specificity of 80.8%. The LVEF-ECG feature had an AUC of 0.908 (95% CI, 0.902-0.913), sensitivity of 83.5%, and specificity of 83.0%. NT-ProBNP had an AUC of 0.740 (95% CI, 0.727-0.752), with a sensitivity of 74.8% and specificity of 62.0%. The ECG-based predictors demonstrated superior diagnostic performance compared to NT-ProBNP (all P<0.001). In the sinus rhythm subgroup, the LV dysfunction score achieved an AUC of 0.913 and LVEF-ECG had an AUC of 0.917, both outperforming NT-ProBNP (AUC, 0.748; 95% CI, 0.732-0.763; all P<0.001).
conclusionECG Buddy demonstrated superior accuracy compared with NT-ProBNP in predicting LV systolic dysfunction, validating its utility in a US ED population.
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