Cardiovascular

Identification of Disulfidptosis-Related Genes in Ischemic Stroke by Combining Single-Cell Sequencing, Machine Learning Algorithms, and In Vitro Experiments.

BACKGROUND: Ischemic stroke (IS) is a severe neurological disorder with a pathogenesis that remains ...

Clinical value of artificial intelligence 3D echocardiography in evaluating left atrial volume and pulmonary vein structure in patients with atrial fibrillation.

OBJECTIVE: To explore the clinical value of 3D Echocardiography (3DE) in evaluating the changes of l...

Development and validation of cardiometabolic risk predictive models based on LDL oxidation and candidate geromarkers from the MARK-AGE data.

The predictive value of the susceptibility to oxidation of LDL particles (LDLox) in cardiometabolic ...

Perforator Selection with Computed Tomography Angiography for Unilateral Breast Reconstruction: A Clinical Multicentre Analysis.

: Despite CTAs being critical for preoperative planning in autologous breast reconstruction, experie...

Clinical impact of deep learning-derived intravascular ultrasound characteristics in patients with deferred coronary artery.

Prognostic markers for long-term outcomes are lacking in patients with deferred (nonculprit) coronar...

Future perspectives and challenges of artificial intelligence implementation in remote monitoring of cardiac implantable electronic devices.

Application of AI-empowered scenario-based simulation teaching mode in cardiovascular disease education.

BACKGROUND: Cardiovascular diseases present a significant challenge in clinical practice due to thei...

Automatic segmentation of echocardiographic images using a shifted windows vision transformer architecture.

Echocardiography is one the most commonly used imaging modalities for the diagnosis of congenital he...

Artificial Intelligence Driven Prehospital ECG Interpretation for the Reduction of False Positive Emergent Cardiac Catheterization Lab Activations: A Retrospective Cohort Study.

OBJECTIVES: Data suggest patients suffering acute coronary occlusion myocardial infarction (OMI) ben...

A deep learning phase-based solution in 2D echocardiography motion estimation.

In this paper, we propose a new deep learning method based on Quaternion Wavelet Transform (QWT) pha...

The value of CCTA combined with machine learning for predicting angina pectoris in the anomalous origin of the right coronary artery.

BACKGROUND: Anomalous origin of coronary artery is a common coronary artery anatomy anomaly. The ano...

Enhancing reginal wall abnormality detection accuracy: Integrating machine learning, optical flow algorithms, and temporal convolutional networks in multi-view echocardiography.

BACKGROUND: Regional Wall Motion Abnormality (RWMA) serves as an early indicator of myocardial infar...

Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-dimensional deep learning segmentation model.

BACKGROUND: Deep learning is the state-of-the-art approach for automated segmentation of the left ve...

Cardiovascular disease diagnosis: a holistic approach using the integration of machine learning and deep learning models.

BACKGROUND: The incidence and mortality rates of cardiovascular disease worldwide are a major concer...

Logistic regression analysis and machine learning for predicting post-stroke gait independence: a retrospective study.

This study investigated whether machine learning (ML) has better predictive accuracy than logistic r...

Mobil Monitoring Doppler Ultrasound (MoMDUS) study: protocol for a prospective, observational study investigating the use of artificial intelligence and low-cost Doppler ultrasound for the automated quantification of hypertension, pre-eclampsia and fetal growth restriction in rural Guatemala.

INTRODUCTION: Undetected high-risk conditions in pregnancy are a leading cause of perinatal mortalit...

Prediction of poststroke independent walking using machine learning: a retrospective study.

BACKGROUND: Accurately predicting the walking independence of stroke patients is important. Our obje...

Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a multicenter, prospective cohort study.

BACKGROUND: The prognosis, recurrence rates, and secondary prevention strategies varied significantl...

A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.

BACKGROUND: Stroke is a leading cause of death and disability worldwide. Rapid and accurate diagnosi...

Predicting Late Gadolinium Enhancement of Acute Myocardial Infarction in Contrast-Free Cardiac Cine MRI Using Deep Generative Learning.

BACKGROUND: Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is a standard techniq...

From Gadolinium to Generative AI: The Quest for Contrast-Free Cardiac Imaging.