Latest AI and machine learning research in arrhythmias for healthcare professionals.
While multimodal large language models (MLLMs) excel in medical applications, most of them favor sta...
\textbf{Background and Objective}: Reliable atrial fibrillation (AF) detection from electrocardiogra...
Federated multimodal models often assume every site has every modality, although hospitals differ in...
Abstract Background: Every non-invasive continuous glucose monitoring (NI-CGM) technology introduced...
Importance: Consumer wearables such as the Apple Watch can record single-lead electrocardiograms (EC...
Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals ...
Self-supervised electrocardiogram (ECG) models are often trained on a few seconds of ECG signal and,...
Electrocardiography (ECG), photoplethysmography (PPG), and phonocardiography (PCG) provide complemen...
Cardiologists interpret electrocardiograms by localizing waveform components, measuring rhythm and i...
Humans converge on shared names for novel, hard-to-describe objects through repeated interaction, a ...
Purpose: To build a 3D U-Net model, BioDeformUNet, to predict the deformation vector field (DVF) of ...
Microwave ablation (MWA) is a promising minimally invasive treatment for liver tumors, but its thera...
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with incr...
Adaptive conformal prediction can recover clinically important heartbeat classes missed by a point c...
Point-of-care cardiac devices such as smartwatches and handheld ECG recorders typically capture 1--2...
Background: Interoperable clinical decision support system (CDSS) rules provide a pathway to interop...
Deep learning has shown promise for automated tongue diagnosis in traditional Chinese medicine (TCM)...
Existing benchmarks for electrocardiogram foundation models primarily evaluate downstream predictive...
Background: Regurgitant valvular heart disease (rVHD) is a major cause of cardiovascular morbidity. ...
As neural network models for image classification advance, neurons play critical roles in pruning, b...
Deep learning models for ECG image classification may achieve high accuracy by exploiting non-physio...