Published on April 30, 2026
Traditionally, assessing left ventricular ejection fraction (LVEF) has relied heavily on echocardiography, making it challenging to perform in primary care settings, especially where resources are scarce. The reliance on such specialized equipment has limited access to critical heart health assessments for many patients.
Recent advancements propose a solution through a multimodal machine-learning framework that integrates 12-lead ECG data with electronic health record (EHR) variables. This innovative approach classifies LVEF into four categories: normal, mildly reduced, moderately reduced, and severely reduced, using extensive data from Hartford HealthCare.
The framework was trained on a dataset comprising 36,784 ECG-echocardiogram pairs from over 30,000 patients. It achieved impressive accuracy with area under the receiver operating characteristic curves (AUROCs) of 0.95 for severe cases and 0.91 for normal cases, significantly outpacing models relying solely on ECG or EHR data.
This development not only enhances diagnostic capabilities but also increases the accessibility of heart health assessments in diverse healthcare environments. screening process, the model prioritizes patients who require further imaging, ultimately leading to improved patient care where resources are limited.
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