ArticleNature communications2023
Cross-modal autoencoder framework learns holistic representations of cardiovascular state.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- A meta-learning method with reduced data requirements for training and updating deep learning models on wearable devices.Communications medicine · 2026Article
- Genetic analysis of imaging-derived phenotypes.Nature reviews. Genetics · 2026Review
- Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Hundreds of cardiac MRI traits derived using 3D diffusion autoencoders share a common genetic architecture.Nature communications · 2026Article
- Prioritizing Discovery and Advancements in Arrhythmia Therapies: NIH/NHLBI Workshop.JACC. Clinical electrophysiology · 2026Review
- Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Multimodal deep learning with missing data robustness for enhanced early diagnosis of coronary artery disease using CCTA, clinical, and ECG data.BMC medical informatics and decision making · 2026Article
- Prediction of coronary artery disease using retinal optical coherence tomography angiography imaging and electronic health records: a multimodal machine learning approach.BMC cardiovascular disorders · 2026Article
- CARDIAC-FM: A Multimodal Foundation Model for Cardiovascular Risk Prediction Using ECG and Cardiac MRI.medRxiv : the preprint server for health sciences · 2026Article
- Partially shared multi-modal embedding learns holistic representation of cell state.Nature computational science · 2026Article
- Deep learning to predict left ventricular hypertrophy from the electrocardiogram.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026Article
- Cardiovascular-kidney-metabolic syndrome: candidate subtypes and genetic risk factors.BMC medical genomics · 2026Article
- Integration of dilated cardiomyopathy genomics with transcriptomics from the human heart implicates regulatory molecular mechanisms.medRxiv : the preprint server for health sciences · 2026Article
- uFlowAM: an unsupervised framework for detection and visualization of abnormal intracardiac microflow on early-pregnancy fetal cardiac microflow imaging.Frontiers in pediatrics · 2026Article
- Integration of clinical data with scanned ECGs using deep learning methods for stroke risk prediction in Indian patients with atrial fibrillation: evidence from the KERALA-AF study.The Lancet regional health. Southeast Asia · 2026Article
- A Representation Fusion Framework for Decoupling Diagnostic Information in Multimodal Learning.NPJ digital medicine · 2025Article
- Personalized cognitive enhancement for older adults: An aging-friendly closed-loop human-machine interface framework.Ageing research reviews · 2025Review
- Noradrenergic Slow Vasomotion: The Hidden Fluid Pump Linking Sleep, Brain Clearance, and Dementia Pathogenesis.International journal of molecular sciences · 2025Review
- Single-cell multiomics data integration and generation with scPairing.Cell reports methods · 2025Article
- Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action.Cell systems · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results systematically integrate distinct diagnostic modalities into a common representation that better characterizes physiologic state.
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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 OpenQuestion graph.