Atherosclerosis

Performance of deep-learning reconstruction combined with metal artifact reduction algorithm for dual-energy computed tomography angiography in intracranial aneurysm coil embolization.

PURPOSES: To evaluate the diagnostic confidence in cerebral aneurysm embolization coil follow-up using the deep learning image reconstruction (DLIR) based virtual monoenergetic images (VMI) combined with metal artifact reduction (MAR) algorithm, with a focus on selecting the most optimal scheme. METHODS: A CTA database of 54 patients was prospectively assembled and reconstructed utilizing adaptive...

Clinical Phenotypes in Hypertension: A Data-Driven Approach to Risk Stratification.

BACKGROUND: Hypertension is a major contributor to cardiovascular morbidity and mortality. Its heterogeneity complicates risk stratification. Unsupervised machine learning can uncover risk profiles and refine preventative strategies. This study applied a data-driven approach to identify clinical phenotypes of hypertension, examine their associations with cardiovascular imaging characteristics and ...

Development and Validation of an Interpretable Machine Learning Model for Predicting Early Fusion After Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: Retrospective case-control study. OBJECTIVES: This study aimed to develop and preliminarily validate a machine learning (ML) model for p...

Explainable machine learning-driven predictive modelling of incident cognitive impairment in individuals with early-stage cardiovascular-kidney-metabolic syndrome: Insights from a longitudinal CHARLS cohort study.

AIMS: While cardiovascular-kidney-metabolic (CKM) syndrome has been recognised as a continuum of interconnected metabolic, renal, and cardiovascular d...

Clinical utility of an AI-based retinal imaging model for cardiovascular risk prediction in hypertensive retinopathy.

OBJECTIVE: This study presents an independent clinical evaluation of Dr.Noon CVD, a commercially developed artificial intelligence (AI)-based retinal ...

Omics-based computational approaches for biomarker identification, prediction, and treatment of Long COVID.

Long COVID, or post-acute sequelae of COVID-19 (PASC), is a major global health problem, with cumulative estimates suggesting that around 400 million ...

Machine learning-based binary classification of elevated HbA1c (≥6.5 %) for risk assessment.

OBJECTIVE: One of the most important biomarkers for evaluating long-term glycemic management and estimating the risk of diabetes is glycated hemoglobi...

Systematic machine learning approach for cerebral aneurysm feature selection and rupture status classification.

BACKGROUND: Current methods of intracranial aneurysm rupture risk assessment in the clinical setting depend on user measurements of morphological fact...

Jointly Learned 3D Non-Cartesian Sampling With Wave Encoding and Reconstruction for Neurovascular Phase Contrast MRI.

PURPOSE: To develop accelerated 3D phase contrast (PC) MRI using jointly learned wave encoding and reconstruction. METHODS: Pseudo-fully sampled neuro...

Predictors of glycemic control with imeglimin for type 2 diabetes: Results of machine learning analyses using clinical trial data.

INTRODUCTION: Identifying patient characteristics predictive of treatment response is crucial for optimizing type 2 diabetes outcomes. Using data from...

Sex-specific cardiovascular risk prediction using AI-derived epicardial adipose tissue measurements on CT calcium scoring exams.

BACKGROUND: The Agatston CAC score from CT-calcium scoring (CTCS) is a standard guideline recommended measure for cardiovascular risk assessment that ...

Comparative performance of machine learning vs classical formulas for LDL-cholesterol calculation.

INTRODUCTION: Low-density lipoprotein cholesterol (LDL-C) is a significant cardiovascular risk factor, as direct measurement is expensive and often un...

Major depressive disorder and Hashimoto's thyroiditis: Shared immunometabolic signatures revealed by integrative transcriptomics.

Major depressive disorder (MDD) and Hashimoto's thyroiditis (HT) frequently co-occur, yet their shared molecular underpinnings remain unclear. We perf...

Risk Models for Adverse Events in Microsurgery for Intracranial Unruptured Aneurysms.

BACKGROUND AND OBJECTIVES: Preventive treatment of unruptured intracranial aneurysms (UIAs) requires assessment of treatment risks vs expected benefit...

Development and validation of a novel machine learning-based algorithm to predict incident atrial fibrillation: A multicohort analysis.

BACKGROUND: Existing atrial fibrillation (AF) risk prediction models incorporate race as a covariate, systematically underestimating AF risk in black ...

Statin-Induced Decade-Long Stabilization of Noncalcified Coronary Plaque Despite a Zero Calcium Score.

BACKGROUND: A 48-year-old man with a coronary artery calcium (CAC) score of 0 underwent serial artificial intelligence (AI)-assisted coronary computed...

Precision medicine in Moyamoya vasculopathy.

PURPOSE OF REVIEW: Moyamoya vasculopathy is a progressive cerebrovascular steno-occlusive disease with variable presentation. As revascularization tec...

Precision engineering of anti-atherosclerotic herbal nanomedicine: from machine learning-aided active components screening to optimized metal-phenolic network codelivery.

Atherosclerosis (AS), a chronic inflammatory disease linked to oxidative stress and lipid imbalance, remains a major cardiovascular threat. Traditiona...

GN-Net: A Geometric and Neighborhood-Aware Network for Predicting Intracranial Aneurysm Rupture Risk and Assisting Clinical Decision-Making.

Gut microbiome profiles and associated functional pathways are linked to Mediterranean diet adherence and blood glucose control in adults with type 1 diabetes mellitus.

BACKGROUND AND AIMS: The Mediterranean diet (MD) has been associated with better glycaemic control in children with type 1 diabetes mellitus (T1DM) an...