Cardiovascular

Dyslipidemia

Latest AI and machine learning research in dyslipidemia for healthcare professionals.

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Predicting dyslipidemia incidence: unleashing machine learning algorithms on Lifestyle Promotion Project data.

BACKGROUND: Dyslipidemia, characterized by variations in plasma lipid profiles, poses a global healt...

Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening.

Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluid...

Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs.

Ischemic stroke is a major cause of mortality worldwide. Proper etiological subtyping of ischemic st...

Machine Learning Detects Symptomatic Plaques in Patients With Carotid Atherosclerosis on CT Angiography.

BACKGROUND: This study aimed to develop and validate a computed tomography angiography based machine...

Improving the Detection of Potential Cases of Familial Hypercholesterolemia: Could Machine Learning Be Part of the Solution?

BACKGROUND: Familial hypercholesterolemia (FH), while highly prevalent, is a significantly underdiag...

Lipids balance as a spectroscopy marker of diabetes. Analysis of FTIR spectra by 2D correlation and machine learning analyses.

The number of people suffering from type 2 diabetes has rapidly increased. Taking into account, that...

Machine learning survival prediction using tumor lipid metabolism genes for osteosarcoma.

Osteosarcoma is a primary malignant tumor that commonly affects children and adolescents, with a poo...

Artificial intelligence in therapeutic management of hyperlipidemic ocular pathology.

Hyperlipidemia has many ocular manifestations, the most prevalent being retinal vascular occlusion. ...

Predicting Non-Alcoholic Steatohepatitis: A Lipidomics-Driven Machine Learning Approach.

Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Weste...

Machine Learning Identification of Nutrient Intake Variations across Age Groups in Metabolic Syndrome and Healthy Populations.

This study undertakes a comprehensive examination of the intricate link between diet nutrition, age,...

Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?

PURPOSE OF REVIEW: This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic c...

A new machine learning model to predict the prognosis of cardiogenic brain infarction.

Cardiogenic cerebral infarction (CCI) is a disease in which the blood supply to the blood vessels in...

Identifying Cardiovascular Disease Risk Endotypes of Adolescent Major Depressive Disorder Using Exploratory Unsupervised Machine Learning.

OBJECTIVE: Adolescents with major depressive disorder (MDD) are at increased risk of premature ather...

Risk prediction model of metabolic syndrome in perimenopausal women based on machine learning.

INTRODUCTION: Metabolic syndrome (MetS) is considered to be an important parameter of cardio-metabol...

Development and multinational validation of an algorithmic strategy for high Lp(a) screening.

Elevated lipoprotein (a) (Lp(a)) is associated with premature atherosclerotic cardiovascular disease...

Radiomics and artificial intelligence: General notions and applications in the carotid vulnerable plaque.

Carotid atherosclerosis plays a substantial role in cardiovascular morbidity and mortality. Given th...

Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes.

Almost 35 years after its introduction, coronary artery calcium score (CACS) not only survived techn...

Cryo-EM images of phase-separated lipid bilayer vesicles analyzed with a machine-learning approach.

Lateral lipid heterogeneity (i.e., raft formation) in biomembranes plays a functional role in living...

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