Foundation model that encodes single-lead (lead I) ECGs with information from paired transthoracic echocardiography reports, aimed at label-efficient screening for structural heart disease (SHD) on wearable and portable single-lead ECG devices. A 7-layer 1D-CNN ECG encoder and a RoBERTa-based text encoder are contrastively pretrained (CLIP-style) on 194,551 ECG-echo report pairs from 77,378 adults in the Yale New Haven Health System, then the ECG encoder is fine-tuned on a temporally-distinct cohort to detect reduced LVEF, diastolic dysfunction, and a composite SHD label. Matches a randomly-initialized CNN at full training-data volume but substantially outperforms it in label-scarce regimes (e.g. with only 0.5% of labeled data).

CNN-based image classifier that screens 12-lead ECG images for a signature of transthyretin amyloid cardiomyopathy (ATTR-CM), producing a study-level probability score. Trained on a private Yale New Haven Health System cohort of nuclear-imaging-confirmed ATTR-CM cases and matched controls. Used alongside a companion echocardiography model to track pre-clinical ATTR-CM progression years before it would otherwise be confirmed by nuclear amyloid imaging. Developed by Yale's CarDS Lab and distributed as a packaged research-use executable rather than downloadable weights.

EfficientNet-B3 CNN that detects hypertrophic cardiomyopathy directly from images of printed or scanned 12-lead ECGs, rather than from raw digital waveforms, enabling screening from a photo of a paper tracing. Initialized via self-supervised contrastive pretraining on patient identity, then fine-tuned at Yale New Haven Hospital on over 124,000 ECGs from about 67,000 patients, with HCM status confirmed by cardiac MRI or echocardiography. Externally validated on ECG images from MIMIC-IV, Amsterdam UMC, and UK Biobank. Developed by Yale's CarDS Lab.

Detects 12 categories of echocardiogram-confirmed structural heart disease from 12-lead ECG waveforms combined with demographic and clinical covariates. Uses the same architecture as the original, larger EchoNext model but is trained entirely on the public EchoNext-Mini dataset - 100,000 de-identified ECGs from Columbia University Irving Medical Center released on PhysioNet - making it one of the more fully reproducible models of its kind, with public weights, a Docker image, and inference code.

CLIP-style model that aligns 12-lead ECG signals with free-text echocardiography reports for zero-shot detection of structural heart disease directly from an ECG. Extends the MERL framework, and was trained on 45,016 paired ECG-echo reports from two Hong Kong hospitals, with external validation on the public EchoNext dataset from Columbia University. Developed by researchers at the University of Hong Kong and Imperial College London; described in a 2025 medRxiv preprint.

BEiT-base Vision Transformer that embeds images of standard 12-lead ECG printouts into a representation space, enabling zero-shot screening for structural heart disease by comparing a new ECG against reference case/control embedding centroids rather than requiring task-specific training. Trained on private Yale New Haven Health System ECG images and validated against the public EchoNext dataset. Aimed at scanned or legacy ECG images still common in EHR systems. Developed by Yale's CarDS Lab.