An ECG foundation model for generalizable cardiac function prediction across the lifespan
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Background
Artificial intelligence-enhanced electrocardiography (AI-ECG) enables scalable, low-cost cardiac dysfunction screening, but existing models are annotation-intensive and predominantly adult-derived, leaving paediatric generalizability uncertain. Paediatric cohorts exhibit highly variable cardiac morphology and function compared to adults, which may be useful for learning generalizable AI-ECG models.
Methods
We pretrained ECG-Fyler on a predominantly paediatric, all-age cohort at Boston Children’s Hospital (1992–2023), annotated with a cardiologyspecific coding system (Fyler codes), and evaluated it on assessments from echocardiography (echo) and cardiac magnetic resonance (CMR) studies. We validated on an external adult cohort from Columbia University Irving Medical Center. Performance was benchmarked against several AI-ECG foundation models by AUROC across age groups, lesion types, and limited-data scenarios.
Findings
The pretraining cohort comprised 782,138 ECGs from 255,271 patients (median age: 10.9 years, IQR: [2.8–16.8]). Internal evaluation included 178,495 ECG-echo pairs (median age: 10.9 [3.7–17.0]) and 8,584 ECG-CMR pairs (median age: 20.7 [15.6–29.6]). External validation included 82,543 ECG-echo pairs from adults (median age: 64.0 [52.0–74.0]). ECG-Fyler improved AUROC across biventricular dysfunction and dilation tasks, with the largest gains in low-data settings. In internal validation, ECG-Fyler detected low left ventricular ejection fraction (LVEF ≤ 40%) from only 100 fine-tuning samples (AUROC: 0.80, 95% CI: [0.78–0.80]), outperforming other models (AUROC < 0.65) and improving with additional fine-tuning (AUROC: 0.94 [0.93–0.94]). Similar improvements were observed for CMR-derived LVEF, RVEF, and ventricular dilation. In external validation on adults, ECG-Fyler exhibited an AUROC of 0.83 (CI: [0.82–0.85]) for LVEF ≤ 40%. After fine-tuning on less than 10% of external data, LVEF ≤ 45% performance (AUROC: 0.87 [0.86–0.88]) outperformed a fully trained, site-specific prior model (AUROC: 0.85 [0.84–0.87]).
Interpretation
Pretraining on richly annotated, paediatric-dominant ECGs yields models that transfer efficiently across institutions and ages, supporting AI-ECG screening and triage when labels or imaging access are limited.
Funding
National Institutes of Health (R01LM012973); Kostin Innovation Fund, Boston Children’s Hospital.
Research in context
Evidence before this study
On April 29, 2026, we searched PubMed from database inception to April 29, 2026, without language restrictions, using the terms “electrocardiogram” AND “foundation model”. We considered original studies describing ECG foundation models, transferable representation- learning approaches, or mixed-age AI-ECG studies relevant to cardiac function prediction, and excluded non-ECG studies, non-original research, and purely task-specific models without a transferable pretraining component. The available evidence consisted mainly of heterogeneous observational development and validation studies, so we did not do a formal meta-analysis. We identified 11 records. Most published artificial intelligence-enhanced electrocardiogram (AI-ECG) foundation models were developed in adult cohorts, whereas paediatric evaluation remained limited. We did not identify a previous report describing a single ECG foundation model trained on a paediatric-dominant, all-age cohort and evaluated across paediatric and adult populations using both echocardiography- and cardiac magnetic resonance-derived measures of ventricular function.
Added value of this study
We developed ECG-Fyler, a clinically grounded ECG foundation model pretrained on 782,138 ECGs from 255,271 patients in a paediatric-dominant, all-age cohort using structured Fyler code annotations. We evaluated transfer learning across echocardiography- and cardiac magnetic resonance-derived tasks, congenital heart disease lesion subgroups, low-resource fine-tuning scenarios, and external adult validation. ECG-Fyler consistently outperformed training from scratch and other ECG foundation-model baselines, with the largest gains when labelled data were scarce, and showed strong cross-age and cross-institution generalization from paediatric pretraining to adult external validation.
Implications of all the available evidence
Taken together, the available evidence suggests that ECG foundation models can improve data efficiency and generalizability, but the field remains dominated by adult data and task-specific applications. Our findings extend this evidence by suggesting that clinically grounded supervised pretraining on a paediatric-dominant, lifespanspanning ECG corpus can support generalizable prediction of ventricular dysfunction and dilation across age groups and institutions. If validated prospectively, such models could support lower-cost screening, triage, and longitudinal monitoring to help prioritize downstream echocardiography or cardiac magnetic resonance imaging, particularly in congenital heart disease and other settings where labelled imaging data are limited.
