Deep learning model that non-invasively estimates cardiac output (CO) from wearable seismocardiography (SCG), a single-lead ECG, and body mass index (BMI), as a potential alternative to invasive right heart catheterization (RHC). Parallel 1D-CNN branches extract features from the SCG and ECG waveforms, which are fused with BMI and passed through a lightweight regression head to predict CO directly. Trained and evaluated via leave-pair-out cross-validation on 73 heart-failure patients (83 RHC encounters) from an open PhysioNet dataset, the model achieved an RMSE of 1.00 L/min (22%) and Pearson correlation of 0.75 versus catheterization-derived CO, with particularly strong performance in low-output states.

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).

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.