Cardiovascular

Using Machine Learning to Predict MACEs Risk in Patients with Premature Myocardial Infarction.

BACKGROUND: The study aimed to develop an interpretable machine learning (ML) model to assess and st...

A novel method of BiFormer with temporal-spatial characteristics for ECG-based PVC detection.

INTRODUCTION: Premature Ventricular Contractions (PVCs) can be warning signs for serious cardiac con...

Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models.

Stroke-associated pneumonia (SAP) is a serious complication of acute ischemic stroke (AIS), signific...

Prediction model of in-hospital mortality risk in intensive care unit patients with cardiac arrest: a multicenter retrospective cohort study based on an ensemble model.

BACKGROUND: In-hospital cardiac arrest (IHCA) is a major adverse event with a high death risk. Machi...

Retraction: Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques.

Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images.

Intracerebral haemorrhage (ICH) is a common form of stroke that affects millions of people worldwide...

Harnessing integrated bioinformatics to identify new diagnostic and therapeutic strategies for heart failure.

Heart failure (HF) is a life-threatening condition that poses a significant challenge on public heal...

Contemporary Prevention and Management of Postoperative Delirium in Cardiac Surgery Patients.

This review examines the prevention and management of postoperative delirium in cardiac surgery pati...

Interpretable machine learning leverages proteomics to improve cardiovascular disease risk prediction and biomarker identification.

BACKGROUND: Cardiovascular diseases (CVDs) rank amongst the leading causes of long-term disability a...

Accuracy of segment anything model for classification of vascular stenosis in digital subtraction angiography.

BACKGROUND: This retrospective study evaluates the diagnostic performance of an optimized comprehens...

Harnessing the hybrid machine learning methods for stroke risk classification.

Stroke is a leading global cause of death, with 80% of cases considered preventable through early de...

Predicting the risk of postoperative gastrointestinal bleeding in patients with Type A aortic dissection based on an interpretable machine learning model.

BACKGROUND: Gastrointestinal bleeding (GIB) is a common complication following Type A aortic dissect...

Ventricular volume adjustment of brain regions depicts brain changes associated with HIV infection and aging better than intracranial volume adjustment.

INTRODUCTION: While the adjustment of intracranial volume (ICV) is reported to have a significant in...

Evaluating Second-Generation Deep Learning Technique for Noise Reduction in Myocardial T1-Mapping Magnetic Resonance Imaging.

BACKGROUND: T1 mapping has become a valuable technique in cardiac magnetic resonance imaging (CMR) f...

Personalizing risk assessment for transcatheter aortic valve replacement: Value of CT imaging and AI.

T2D-LVDD: neural network-based predictive models for left ventricular diastolic dysfunction in type 2 diabetes.

Cardiovascular disease complications are the leading cause of morbidity and mortality in patients wi...

Accelerated deep learning-based function assessment in cardiovascular magnetic resonance.

PURPOSE: To evaluate diagnostic accuracy and image quality of deep learning (DL) cine sequences for ...

Hybrid machine learning for real-time prediction of edema trajectory in large middle cerebral artery stroke.

In treating malignant cerebral edema after a large middle cerebral artery stroke, clinicians need qu...

Cardiovascular imaging in 2024: review of current research and innovations.

Cardiovascular imaging saw significant advancements in 2024, impacting technology, pathophysiology, ...

Seeing through the leak: a global perspective on aortic regurgitation assessment.

AIMS: Despite established guidelines, the echocardiographic quantification of aortic regurgitation (...

Identification of Fatty Acid Metabolism Disorder-Related Gene Signature in Septic Cardiomyopathy.

BACKGROUND: Septic cardiomyopathy (SCM) is a prevalent complication of sepsis and a primary contribu...