Latest AI and machine learning research in congestive heart failure for healthcare professionals.
Diffuse myocardial fibrosis contributes to adverse remodeling and heart failure progression in non-ischemic dilated cardiomyopathy (NIDCM). Quantitative cardiac magnetic resonance (CMR) tissue mapping may improve risk stratification, but manual post-processing limits clinical application. This study aimed to evaluate the prognostic and functional significance of artificial intelligence (AI)–assist...
Several artificial intelligence-enhanced electrocardiogram (AI-ECG) models have shown promise in detecting left ventricular systolic dysfunction (LVSD), but their head-to-head agreement and performance have not been independently compared within the same cohort. To compare the performance of published AI-ECG models for LVSD detection in a standardized external cohort and evaluate the field’s trans...
Pericardial effusion can progress to life-threatening cardiac tamponade when large or rapidly accumulating, yet early diagnosis is frequently delayed ...
To develop and validate machine learning models for predicting Blood Pressure (BP) control status using demographic characteristics and longitudinal B...
Cardiovascular disease (CVD) remains the leading cause of mortality globally, with many events occurring in individuals without prior diagnosed condit...
The accurate non-invasive detection and estimation of central aortic pressure waveforms (CAPW) are crucial for reliable treatments of cardiovascular s...
INTRODUCTION: Stroke patients are at high risk of developing cerebral edema, which can have severe consequences. However, there are currently few effe...
The rapid adaptation of data driven AI models, such as deep learning inference, training, Vision Transformers (ViTs), and other HPC applications, dr...
Vector quantization(VQ) is a hardware-friendly DNN compression method that can reduce the storage cost and weight-loading datawidth of hardware acce...
The segmentation and classification of cardiac magnetic resonance imaging are critical for diagnosing heart conditions, yet current approaches face ...
PURPOSE: Identify optimal metabolic features and pathways across diabetic retinopathy (DR) stages, develop risk models to differentiate diabetic macul...
To explore the value of predicting new-onset heart failure events in patients with hypertrophic cardiomyopathy (HCM) using clinical and cardiac magne...
Traditional Chinese Medicine (TCM) involves complex compatibility mechanisms characterized by multi-component and multi-target interactions, which a...
The left ventricular end-systolic pressure-volume relationship (ESPVr) is a key indicator of cardiac contractility. Despite its established importan...
Modern transformer-based deep neural networks present unique technical challenges for effective acceleration in real-world applications. Apart from ...
Cardiovascular diseases are best diagnosed using multiple modalities that assess both the heart's electrical and mechanical functions. While effecti...
In this paper, we introduce a novel Multi-Modal Contrastive Pre-training Framework that synergistically combines X-rays, electrocardiograms (ECGs), ...
Anterior ST-segment elevation myocardial infarction (STEMI) is associated with severe adverse remodeling and increased mortality rates. In this stud...
Diabetic retinopathy and diabetic macular edema are significant complications of diabetes that can lead to vision loss. Early detection through ultr...
For World Heart Day on September 24, 2024, the World Heart Federation urges nations to endorse national strategies for enhancing cardiovascular health...