Why the study?
Low ejection fraction often goes undiagnosed and can lead to avoidable heart failure and arrhythmias.
Does a deep learning model applied to 12-lead electrocardiograms improve the detection of low ejection fraction compared to default screening approaches?
Does a deep learning model applied to 12-lead electrocardiograms improve the detection of low ejection fraction compared to default screening approaches?
Answers are grounded in this paper and cite their sources.
An ECG-based deep learning model can accurately detect low ejection fraction, providing greater clinical value than default screening approaches and supporting its FDA clearance.
Low ejection fraction (EF), an indicator of impaired heart function, often goes undiagnosed and can lead to avoidable heart failure and arrhythmias. We developed and externally validated a deep learning model for detecting low EF from 12-lead electrocardiograms. The model achieved 85.8% sensitivity and 83.0% specificity on an independent validation cohort, with consistent results across demographic subgroups. These findings supported FDA 510(k) clearance of the model. Clinical net benefit analysis further showed that the model provides greater clinical value than default screening approaches, confirming its meaningful potential impact for clinical practice.
No takes yet. Share an insight, caveat, or question.
Hakim et al. (2025) studied this question.
Synapse has enriched 8 closely related papers on similar clinical questions. Consider them for comparative context: