ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2026
Estimation of Left Ventricular Systolic Function in Pediatric and Congenital Heart Disease from Serial Electrocardiograms.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
5 authors.
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Abstract
Estimating left ventricular ejection fraction (LVEF) from electrocardiograms (ECGs) is a useful task enabled by artificial intelligence (AI-ECG). Prior work focuses on predicting LVEF from single ECGs. Here, we investigate whether a patient's history of ECGs can improve LVEF prediction. We study a longitudinal cohort from Boston Children's Hospital, deriving LVEF from echocardiograms conducted within 2 days of ECGs (n=178,495, n. patients: 70,226, median age: 10.6). We propose a sequential AI-ECG approach using convolutional layers to represent single ECGs and a sequential neural network to reason over longitudinal ECGs. We build and test several sequential architectures. For predictions with at least 5 previous ECGs, sequential AI-ECG improved median AUROC (IQR) by 3.4 points (1.4, 4.4). When predicting the LVEF value, sequential AI-ECG improves Pearson R by 0.08 (0.02, 0.13). Results suggest that patients' longitudinal ECG history contains valuable information for improving AI-ECG risk stratification beyond current snapshot-based models.
Identifiers
42317850PMC13274358What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.