Public articles linked to the same research event.
medRxiv This study developed CARDIAC-FM, a multimodal foundation model that integrates 12-lead ECG and cardiac MRI through self-supervised representation learning and cross-modal contrastive alignment, pretrained on 57,609 paired samples from UK Biobank, improving prediction of incident atrial fibrillation and heart failure, generalizing zero-shot to the Cardiovascular Health Study and the Multi-Ethnic Study of Atherosclerosis, and transferring to cardiac MRI phenotype prediction, time-to-event modelling, and additional outcomes including myocardial infarction, ischaemic stroke, cardiovascular death, and all-cause mortality.
This study developed CARDIAC-FM, a multimodal foundation model that integrates 12-lead ECG and cardiac MRI through self-supervised representation learning and cross-modal contrastive alignment, pretrained on 57,609 paired samples from UK Biobank, improving prediction of incident atrial fibrillation and heart failure, generalizing zero-shot to the Cardiovascular Health Study and the Multi-Ethnic Study of Atherosclerosis, and transferring to cardiac MRI phenotype prediction, time-to-event modelling, and additional outcomes including myocardial infarction, ischaemic stroke, cardiovascular death, and all-cause mortality.
This study developed CARDIAC-FM, a multimodal foundation model that integrates 12-lead ECG and cardiac MRI through self-supervised representation learning and cross-modal contrastive alignment, pretrained on 57,609 paired samples from UK Biobank, improving prediction of incident atrial fibrillation and heart failure, generalizing zero-shot to the Cardiovascular Health Study and the Multi-Ethnic Study of Atherosclerosis, and transferring to cardiac MRI phenotype prediction, time-to-event modelling, and additional outcomes including myocardial infarction, ischaemic stroke, cardiovascular death, and all-cause mortality.
This study developed CARDIAC-FM, a multimodal foundation model that integrates 12-lead ECG and cardiac MRI through self-supervised representation learning and cross-modal contrastive alignment, pretrained on 57,609 paired samples from UK Biobank, improving prediction of incident atrial fibrillation and heart failure, generalizing zero-shot to the Cardiovascular Health Study and the Multi-Ethnic Study of Atherosclerosis, and transferring to cardiac MRI phenotype prediction, time-to-event modelling, and additional outcomes including myocardial infarction, ischaemic stroke, cardiovascular death, and all-cause mortality.