Latest AI and machine learning research in arrhythmias for healthcare professionals.
Explainable AI (XAI) is used to assess whether artificial intelligence models rely on meaningful pat...
Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail ...
Autoregressive foundation models trained on tokenized electronic health records (EHRs) can support z...
Automated electrocardiogram (ECG) interpretation has advanced, yet most systems remain narrow classi...
The electrocardiogram (ECG) is a cornerstone of cardiac as- sessment, yet clinical deployment of dee...
Background Structural heart disease (SHD) drives heart failure and cardiovascular mortality but rema...
We propose a weakly supervised 18FFDG PET representation-learning framework for content based medica...
Adult and pediatric electrocardiogram (ECG) interpretation relies on age-sensitive criteria, and mod...
We conducted a scaling evaluation of unlabeled pretraining for electrocardiogram foundation model pe...
Long-tailed label distributions reduce the reliability of deep learning for electrocardiogram (ECG) ...
Cardiovascular diseases are the leading cause of death worldwide, and conditions such as arrhythmia ...
Background Recent artificial intelligence (AI) models applied to the electrocardiogram (ECG) for ris...
Sparse autoencoders (SAEs) are the standard for decomposing superposed neural representations into i...
The electrocardiogram (ECG) contains rich nonlinear and non-stationary dynamic information that is o...
Background: Heart failure with reduced ejection fraction (HFrEF) remains a major global health burde...
Physics-Informed Neural Networks (PINNs) provide a meshless approach for solving partial differentia...
Adhesive electrocardiography (ECG) electrodes used in neonatal intensive care units (NICUs) may caus...
Fetal electrocardiogram (fECG) and Doppler ultrasound provide complementary views of fetal cardiovas...
Electrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease...
Federated learning (FL) enables collaborative model training across institutions without sharing sen...
Longitudinal dementia progression prediction is essential for clinical decision-making. However, mod...