Multimodal Contrastive Learning with ECG and Echocardiography for Ejection Fraction Estimation

Learning from Scarce Labels: Multi-View Echocardiography for Ejection Fraction Prediction

This work creates the first publicly available resource for predicting left ventricular ejection fraction (EF) from parasternal long-axis (PLAX) echocardiography by leveraging a time-based correlation between clinical notes and echocardiographic videos and fine-tuning view classifiers and proxy labeling.

Weakly supervised anatomical feature learning for cross-dataset ejection fraction estimation from echocardiography videos

The findings support controlled external validation under explicit image and target harmonization, while further prospective validation, calibration, and quality-control mechanisms are needed before clinical deployment.

Machine Learning for Biomedical Imaging Learning from Scarce Labels: Multi-View Echocardiography for Ejection Fraction Prediction

Unified Structural Contrastive Learning for LVEF Estimation in Echocardiograms.

Point-of-care echocardiography screening for hypertrophic cardiomyopathy using automated deep-learning analysis

Abstract Aims Hypertrophic cardiomyopathy (HCM) remains underdiagnosed due to limited access to expert imaging. We developed and validated a deep-learning (DL)-based echocardiographic model adaptable to point-of-care ultrasound (POCUS) for scalable HCM screening. Methods and results We retrospectively analysed 134 956...

EchoDino: A pediatric foundation model for transferable echocardiographic analysis across the lifespan

Echocardiography is the most widely used cardiac imaging modality, yet interpretation demands integrating visual evidence across global anatomy, localized structures and dynamic cardiac motion. Machine-learning models have automated individual tasks, but they are typically built for a single purpose and depend on expen...