Cardiovascular

Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates.

A cardiac digital twin is a virtual replica of a patient-specific heart, mimicking its anatomy and p...

The Role of Artificial Intelligence in Providing Real-Time Guidance During Interventional Cardiology Procedures: A Narrative Review.

Integrating artificial intelligence (AI) in interventional cardiology revolutionizes procedural guid...

Determining the biomarkers and pathogenesis of myocardial infarction combined with ankylosing spondylitis via a systems biology approach.

Ankylosing spondylitis (AS) is linked to an increased prevalence of myocardial infarction (MI). Howe...

Effects of exoskeleton rehabilitation robot training on neuroplasticity and lower limb motor function in patients with stroke.

BACKGROUND: Lower limb exoskeleton rehabilitation robot is a new technology to improve the lower lim...

Prediction of intradialytic hypotension by machine learning: A systematic review.

BACKGROUND: Intradialytic hypotension is associated with increased morbidity, and mortality. Several...

Multitask learning multimodal network for chronic disease prediction.

Chronic diseases are a critical focus in the management of elderly health. Early disease prediction ...

Multi-objective optimization framework to plan laser ablation procedure for prostate tumors through a genetic algorithm.

BACKGROUND AND OBJECTIVES: Prostate cancer is the most common form of cancer in the male population....

A systematic review on deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction.

BACKGROUND: Coronary artery disease (CAD) is a major worldwide health concern, contributing signific...

Optimizing stroke lesion segmentation: A dual-approach using Gaussian mixture models and nnU-Net.

Machine learning-based stroke lesion segmentation models are widely used in biomedical imaging, but ...

Automatic ultrasound image alignment for diagnosis of pediatric distal forearm fractures.

PURPOSE: The study aims to develop an automatic method to align ultrasound images of the distal fore...

Machine Learning Methods to Predicting Transvalvular Gradient Waveform Post-Transcatheter Aortic Valve Replacement Using Pre-procedural Echocardiogram.

OBJECTIVE: Time-varying transvalvular pressure gradient after transcatheter aortic valve replacement...

Detecting the left atrial appendage in CT localizers using deep learning.

Patients with cardioembolic stroke often undergo CT of the left atrial appendage (LAA), for example,...

Semi-Supervised Echocardiography Video Segmentation via Adaptive Spatio-Temporal Tensor Semantic Awareness and Memory Flow.

Accurate segmentation of cardiac structures in echocardiography videos is vital for diagnosing heart...

Feedback Attention to Enhance Unsupervised Deep Learning Image Registration in 3D Echocardiography.

Cardiac motion estimation is important for assessing the contractile health of the heart, and perfor...

High-Resolution Maps of Left Atrial Displacements and Strains Estimated With 3D Cine MRI Using Online Learning Neural Networks.

The functional analysis of the left atrium (LA) is important for evaluating cardiac health and under...

Deep Learning in Echocardiography for Enhanced Detection of Left Ventricular Function and Wall Motion Abnormalities.

Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, underscoring the need ...

Balancing privacy and health integrity: A novel framework for ECG signal analysis in immersive environments.

The widespread use of immersive technologies such as Virtual Reality, Mixed Reality, and Augmented R...

Multimodal Visualization and Explainable Machine Learning-Driven Markers Enable Early Identification and Prognosis Prediction for Symptomatic Aortic Stenosis and Heart Failure With Preserved Ejection Fraction After Transcatheter Aortic Valve Replacement: Multicenter Cohort Study.

BACKGROUND: Currently, there is a paucity of literature addressing personalized risk stratification ...

Application of machine learning in risk assessment of major adverse cardiovascular events following coronary artery bypass grafting: Analysis of model performance and key predictive factors.

Factors influencing short-term and long-term survival rates in stroke patients receiving enteral nutrition: a machine learning approach using MIMIC-IV database.

PURPOSE: This study aims to evaluate the survival and mortality rates of stroke patients after recei...

Deep learning-based classification of coronary arteries and left ventricle using multimodal data for autonomous protocol selection or adjustment in angiography.

Optimal selection of X-ray imaging parameters is crucial in coronary angiography and structural card...