Cardiovascular

Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills.

Health professional education stands to gain substantially from collective efforts toward building v...

Precise ablation zone segmentation on CT images after liver cancer ablation using semi-automatic CNN-based segmentation.

BACKGROUND: Ablation zone segmentation in contrast-enhanced computed tomography (CECT) images enable...

3DECG-Net: ECG fusion network for multi-label cardiac arrhythmia detection.

Cardiovascular diseases represent the leading global cause of death, typically diagnosed and address...

Digital therapeutics in hypertension: How to make sustainable lifestyle changes.

Various digital therapeutic products have been validated and approved since 2017. They have demonstr...

Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism.

Deep learning has shown great promise in predicting Atrial Fibrillation using ECG signals and other ...

The emergence of deep learning as the current state of art for classification and risk assessment of ventricular arrhythmias.

Rule-based natural language processing to examine variation in worsening heart failure hospitalizations by age, sex, race and ethnicity, and left ventricular ejection fraction.

BACKGROUND: Prior studies characterizing worsening heart failure events (WHFE) have been limited in ...

Prediction of sudden cardiac death using artificial intelligence: Current status and future directions.

Sudden cardiac death (SCD) remains a pressing health issue, affecting hundreds of thousands each yea...

Association of N-terminal pro-B natriuretic peptide with all-cause mortality and cardiovascular mortality in obese and non-obese populations and the development of a machine learning prediction model: National Health and Nutrition Examination Survey (NHANES) 1999-2004.

AIMS: To explore the potential of N-terminal pro-B natriuretic peptide (NTproBNP) in identifying adv...

Development and validation of a machine learning-based approach to identify high-risk diabetic cardiomyopathy phenotype.

AIMS: Abnormalities in specific echocardiographic parameters and cardiac biomarkers have been report...

Enhancing Upper Limb Function and Motor Skills Post-Stroke Through an Upper Limb Rehabilitation Robot.

Cerebrovascular accidents, commonly known as strokes, represent a prevalent neurological event leadi...

Mesocorticolimbic and Cardiometabolic Diseases-Two Faces of the Same Coin?

The risk behaviors underlying the most prevalent chronic noncommunicable diseases (NCDs) encompass a...

A coordinated adaptive multiscale enhanced spatio-temporal fusion network for multi-lead electrocardiogram arrhythmia detection.

The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of ca...

Artificial Intelligence in the Hands of Perfusionists: Revolutionizing Cardiopulmonary Bypass.

Artificial intelligence-assisted measurements of coronary computed tomography angiography parameters such as stenosis, flow reserve, and fat attenuation for predicting major adverse cardiac events in patients with coronary arterial disease.

Advancements in artificial intelligence (AI) offer promising tools for improving diagnostic accuracy...

Accelerated chemical shift encoded cardiovascular magnetic resonance imaging with use of a resolution enhancement network.

BACKGROUND: Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial...

Hematoma expansion prediction in intracerebral hemorrhage patients by using synthesized CT images in an end-to-end deep learning framework.

Spontaneous intracerebral hemorrhage (ICH) is a type of stroke less prevalent than ischemic stroke b...

Deep learning method with integrated invertible wavelet scattering for improving the quality ofcardiac DTI.

Respiratory motion, cardiac motion and inherently low signal-to-noise ratio (SNR) are major limitati...

SeqAFNet: A Beat-Wise Sequential Neural Network for Atrial Fibrillation Classification in Adhesive Patch-Type Electrocardiographs.

Due to their convenience, adhesive patch-type electrocardiographs are commonly used for arrhythmia s...

RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG.

INTRODUCTION: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm i...

Long-term Major Adverse Cardiac Event Prediction by Computed Tomography-derived Plaque Measures and Clinical Parameters Using Machine Learning.

Objective The present study evaluated the usefulness of machine learning (ML) models with the corona...