Cardiovascular

Peripheral Artery Disease

Latest AI and machine learning research in peripheral artery disease for healthcare professionals.

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Showing 85-105 of 7,574 articles
Contrast-enhanced magnetic resonance imaging based calf muscle perfusion and machine learning in peripheral artery disease.

Peripheral artery disease (PAD) remains underdiagnosed and undertreated and is associated with an in...

TQGDNet: Coronary artery calcium deposit detection on computed tomography.

Coronary artery disease (CAD) continues to be a leading global cause of cardiovascular related morta...

Statin use and longitudinal bone marrow lesion burden: analysis of knees without osteoarthritis from the Osteoarthritis Initiative study.

OBJECTIVES: Knee subchondral bone marrow lesions (BMLs) are one of the hallmark features of structur...

Using machine learning to predict outcomes following transcarotid artery revascularization.

Transcarotid artery revascularization (TCAR) is a relatively new and technically challenging procedu...

Machine Learning Analysis of Nutrient Associations with Peripheral Arterial Disease: Insights from NHANES 1999-2004.

BACKGROUND: Peripheral arterial disease (PAD) is a common manifestation of atherosclerosis, affectin...

Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection.

Revascularization therapies, such as percutaneous coronary intervention (PCI) and coronary artery by...

Advances in the Application of Artificial Intelligence in the Ultrasound Diagnosis of Vulnerable Carotid Atherosclerotic Plaque.

Vulnerable atherosclerotic plaque is a type of plaque that poses a significant risk of high mortalit...

Patch-Wise Deep Learning Method for Intracranial Stenosis and Aneurysm Detection-the Tromsø Study.

Intracranial atherosclerotic stenosis (ICAS) and intracranial aneurysms are prevalent conditions in ...

Diagnostic accuracy in coronary CT angiography analysis: artificial intelligence versus human assessment.

BACKGROUND: Visual assessment of coronary CT angiography (CCTA) is time-consuming, influenced by rea...

Automated stenosis estimation of coronary angiographies using end-to-end learning.

The initial evaluation of stenosis during coronary angiography is typically performed by visual asse...

Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers.

This study proposes a framework to stratify vascular disease patients based on brain health and cere...

Predicting host health status through an integrated machine learning framework: insights from healthy gut microbiome aging trajectory.

The gut microbiome, recognized as a critical component in the development of chronic diseases and ag...

Using clinical data to reclassify ESUS patients to large artery atherosclerotic or cardioembolic stroke mechanisms.

PURPOSE: Embolic stroke of unidentified source (ESUS) represents 10-25% of all ischemic strokes. Our...

sJAM-C as a Potential Biomarker for Coronary Artery Stenosis: Insights from a Clinical Study in Coronary Heart Disease Patients.

PURPOSE: Coronary artery stenosis caused by atherogenesis is a major pathological link in coronary h...

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