Cardiovascular

Point-of-care AI-enhanced novice echocardiography for screening heart failure (PANES-HF).

The increasing prevalence of heart failure (HF) in ageing populations drives demand for echocardiogr...

Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning.

Functionally relevant coronary artery disease (fCAD) can result in premature death or nonfatal acute...

A three-year update on guidelines for upper limb robotic rehabilitation after stroke.

Artificial Intelligence-Based Atrial Fibrillation Recognition Method for Motion Artifact-Contaminated Electrocardiogram Signals Preprocessed by Adaptive Filtering Algorithm.

Atrial fibrillation (AF) is a common arrhythmia, and out-of-hospital, wearable, long-term electrocar...

Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface.

Nifedipine (NIF) is a dihydropyridine calcium channel blocker primarily used to treat conditions suc...

Development and validation of a machine learning-based readmission risk prediction model for non-ST elevation myocardial infarction patients after percutaneous coronary intervention.

To investigate the factors that influence readmissions in patients with acute non-ST elevation myoca...

Semi-supervised learning framework with shape encoding for neonatal ventricular segmentation from 3D ultrasound.

BACKGROUND: Three-dimensional (3D) ultrasound (US) imaging has shown promise in non-invasive monitor...

Analysis of Cerebral CT Based on Supervised Machine Learning as a Predictor of Outcome After Out-of-Hospital Cardiac Arrest.

BACKGROUND AND OBJECTIVES: In light of limited intensive care capacities and a lack of accurate prog...

Towards deep learning methods for quantification of the right ventricle using 2D echocardiography.

Machine learning models' assessment: trust and performance.

The common black box nature of machine learning models is an obstacle to their application in health...

Explainable deep-learning-based ischemia detection using hybrid O-15 HO perfusion positron emission tomography and computed tomography imaging with clinical data.

BACKGROUND: We developed an explainable deep-learning (DL)-based classifier to identify flow-limitin...

Precise risk-prediction model including arterial stiffness for new-onset atrial fibrillation using machine learning techniques.

Atrial fibrillation (AF) is the most common clinically significant cardiac arrhythmia and is an impo...

Exoskeleton rehabilitation robot training for balance and lower limb function in sub-acute stroke patients: a pilot, randomized controlled trial.

PURPOSE: This pilot study aimed to investigate the effects of REX exoskeleton rehabilitation robot t...

Digital twins: reimagining the future of cardiovascular risk prediction and personalised care.

The rapid evolution of highly adaptable and reusable artificial intelligence models facilitates the ...

Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research.

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy ...

An Experiment Using Functional Near-Infrared Spectroscopy and Robot-Assisted Multi-Joint Pointing Movements of the Lower Limb.

Stroke affects approximately 17 million individuals worldwide each year and is a leading cause of lo...

A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission.

BACKGROUND: This study aimed to develop a higher performance nomogram based on explainable machine l...

Automated assessment of cardiac dynamics in aging and dilated cardiomyopathy Drosophila models using machine learning.

The Drosophila model is pivotal in deciphering the pathophysiological underpinnings of various human...

xECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features.

Deep learning-based methods have demonstrated high classification performance in the detection of ca...

Artificial Intelligence for Automatic Analysis of Shunt Treatment in Presurgery and Postsurgery Computed Tomography Brain Scans of Patients With Idiopathic Normal Pressure Hydrocephalus.

BACKGROUND AND OBJECTIVES: Ventriculo-peritoneal shunt procedures can improve idiopathic normal pres...

Volumetric analysis of acute uncomplicated type B aortic dissection using an automated deep learning aortic zone segmentation model.

BACKGROUND: Machine learning techniques have shown excellent performance in three-dimensional medica...