Cross-modality cardiac image segmentation model that addresses spatial-temporal confounding -- where the anatomy and imaging-modality elements of cardiac images are intertwined across space and time. DCL performs multi-dimensional causal intervention, modeling causal relationships between images and labels as well as causality along the time and space dimensions, integrating historical optimal interventions to transfer knowledge across temporal contexts. A diffusion mechanism further keeps extracted anatomical elements causally invariant across modalities. On cross-modality cardiac images (MR, CT, and ultrasound), DCL achieved a mean Dice of 0.951, outperforming other advanced segmentation methods.

Self-supervised deep learning model that extracts cardiovascular-risk-relevant patterns from multimodal polysomnography (PSG) signals -- EEG, ECG, and respiratory signals -- without relying on manual sleep-stage annotations. Trained on 4,398 participants, the model derives 'projection scores' by contrasting embeddings from individuals with and without cardiovascular disease (CVD) outcomes. Externally validated in an independent cohort of 1,093 participants, ECG-derived projection scores were predictive of prevalent and incident cardiac conditions (particularly CVD mortality), and combining projection scores with the Framingham Risk Score consistently improved prediction (AUC 0.607-0.965 internally, 0.710-0.807 externally across most outcomes).

Real-time deep-learning model that fuses serial 12-lead ECG waveforms with sequential vital signs and routinely available clinical data to predict hospital admission early during emergency department (ED) encounters with cardiac presentations (chest pain, dyspnea, syncope, presyncope). Developed and validated on the public MIMIC-IV, MIMIC-IV-ED, and MIMIC-IV-ECG databases (n=30,421 ED stays with >=1 ECG; n=11,273 with >=2 ECGs), the model improves on baseline tabular (random forest) and ECG-only models by leveraging how a patient's risk evolves with successive ECGs during the visit, addressing a key limitation of single-time-point risk scores.

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.

Multimodal machine learning model that flags patients at risk of transthyretin amyloid cardiomyopathy (ATTR-CM) -- a progressive, underdiagnosed disease with expanding disease-modifying treatment options -- from routinely available ECG waveforms, echocardiographic measurements, demographics, and diagnosis codes for orthopedic manifestations of amyloidosis (e.g. carpal tunnel syndrome, spinal stenosis). Developed on 799 patients with 5-fold cross-validation (AUROC 0.85) and externally validated on 422 patients at a separate site (AUROC 0.82), with consistent accuracy across Hispanic, non-Hispanic Black, and non-Hispanic White patients. In a subsequent nonrandomized, single-system, multisite clinical trial (the Cardiac Amyloidosis Discovery Trial), patients flagged by ATTRACTnet and referred for confirmatory amyloid scintigraphy were positive for ATTR-CM 48% of the time, more than 2.8x the positivity rate of historical (15.3%) and contemporary (17.0%) controls referred by usual clinical judgment (P < .001 for both). This is a proprietary clinical AI program; no public code or model weights have been released.

Multimodal large language model for ECG medical-report generation and cardiology conversational question-answering. An ECG-CoCa encoder (contrastive ECG-report pretraining in the style of OpenCLIP) is paired with a LLaVA-style vision-language architecture and an LLM backbone, fine-tuned on a purpose-built 45k-example ECG-instruction dataset (19k diagnosis examples + 25k multi-turn dialogue examples) built from five public 12-lead ECG datasets. Produces free-text diagnostic reports and supports zero-shot ECG-report retrieval classification.

First multimodal LLM to unify ECG time series, 12-lead ECG images, and text for grounded, clinician-aligned ECG interpretation. A dual-encoder framework (ECG-CoCa time-series encoder plus a LLaVA-style vision-language backbone) extracts complementary time-series and image features with cross-modal alignment, trained on knowledge-guided instruction data (ECG-Grounding, linking diagnoses to measurable waveform parameters such as QRS/PR intervals) plus the 1.15-million-conversation ECG-Instruct corpus. Introduces the "Grounded ECG Understanding" benchmark and improves predictive performance, explainability, and grounding over prior ECG-language models such as ECG-Chat and PULSE.

Multimodal cardiac MRI foundation model that fuses 3D+T cine CMR (short-axis and long-axis views) with tabular patient health records (demographics, metabolic, and lifestyle factors) from 42,000 UK Biobank participants. Two-stage self-supervised pretraining -- masked-image reconstruction, then imaging-tabular contrastive alignment -- produces representations that transfer to whole-heart segmentation, cardiac phenotype/physiological-feature regression, and cardiac/metabolic disease classification within one unified framework.

Vision-language model that jointly embeds a cardiac MRI study, treated as video, with the impression section of its clinical report. Combines a video encoder over cine/LGE frame sequences with a Bio+ClinicalBERT text encoder using CLIP-style contrastive training. Supports zero-shot and few-shot classification of cardiomyopathies, amyloidosis, and LV dysfunction, plus image/report retrieval and structured report drafting. Trained on a private, single-institution corpus of roughly 11,000-14,000 CMR study-report pairs from Cleveland Clinic and Case Western.

Multimodal ECG model that pairs a 1D ConvNeXtV2 signal encoder with a BioLinkBERT text encoder, trained with a joint contrastive-and-captioning objective using LLM-generated descriptions of ECG demographics and waveform patterns in place of raw clinical reports. Validated on arrhythmia diagnosis and ECG-based subject identification, reaching an AUROC of 0.938 fine-tuned and 0.812 zero-shot on PTB-XL diagnostic classification. Developed at Rice University.

Vision-language foundation model fine-tuned from CLIP on more than one million private echocardiogram video-report pairs, enabling zero-shot cardiac function assessment, device identification, and image/text retrieval without task-specific training. Combines a ConvNeXt-Base video encoder with a GPT-2-style text encoder under contrastive pretraining. Training data is private, but model weights and code are public. Developed by Cedars-Sinai's Ouyang lab.

Vision-language foundation model that interprets an entire transthoracic echocardiogram study rather than a single view or video: it classifies the view type of every clip, applies view-informed attention across the full study, and generates or retrieves comprehensive study-level interpretations in English or Italian. Pretrained on a private Cedars-Sinai corpus of 12 million echo video-report pairs. Developed by the Smidt Heart Institute and Stanford's Ouyang lab.

Distills knowledge from EchoCLIP, a vision-language echocardiography model, into ECG embeddings, aiming to improve how well ECG signals alone can predict echo-derived measures of cardiac function. Combines a 1D ECG encoder with a BioBERT text encoder under a probabilistic cross-modal embedding objective that captures uncertainty. Published at MICCAI 2025 by the University of Toronto's McIntosh Lab.

Multimodal model that learns a shared representation space for ECG signals and their clinical text reports, pretrained on paired MIMIC-IV-ECG recordings and reports. Supports zero-shot ECG classification via text prompts, tested across six public benchmark datasets including PTB-XL and CPSC2018 without any downstream training data. Developed at Imperial College London and published at ICML 2024.

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.