Atherosclerosis

Enhancing cardiovascular risk prediction: the role of wall viscoelasticity in machine learning models.

This study aims to evaluate the significance of wall viscoelasticity in enhancing cardiovascular disease (CVD) risk prediction. We collected data on ten patient features, categorized into demographics (age, gender, blood pressure, smoking history), blood lab data (HDL, LDL, blood glucose levels), and wall mechanics (Peterson's modulus, stiffness parameter, energy dissipation ratio). Outcome variab...

Early prediction of cardiovascular events following treatments in female breast cancer patients: Application of real-world data and artificial intelligence.

• Application of real-world data and artificial intelligence in detecting cardiotoxicity following cancer treatment. • Clinical features have been used to develop prediction models. • Important features include age, tumor size, hypertension, HbA1c, HDL, creatinine, bilirubin, BUN, ALT, and diabetes. • This study offers potential approaches for cardio-oncology clinical practice.

Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding.

Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and serves as a major...

Artificial intelligence driven plaque characterization and functional assessment from CCTA using OCT-based automation: A prospective study.

BACKGROUND: We aimed to develop and validate an Artificial Intelligence (AI) model that leverages CCTA and optical coherence tomography (OCT) images f...

Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.

Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is...

Machine Learning Models Integrating Dietary Indicators Improve the Prediction of Progression from Prediabetes to Type 2 Diabetes Mellitus.

: Diet plays an important role in preventing and managing the progression from prediabetes to type 2 diabetes mellitus (T2DM). This study aims to deve...

Early prediction of postpartum dyslipidemia in gestational diabetes using machine learning models.

This study addresses a gap in research on predictive models for postpartum dyslipidemia in women with gestational diabetes mellitus (GDM). The goal wa...

Retinal Vascularization Rate Predicts Retinopathy of Prematurity and Remains Unaffected by Low-Dose Bevacizumab Treatment.

PURPOSE: To assess the rate of retinal vascularization derived from ultra-widefield (UWF) imaging-based retinopathy of prematurity (ROP) screening as ...

Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair.

BACKGROUND: Thoracic endovascular aortic repair (TEVAR) and complex endovascular aneurysm repair (EVAR) are complex procedures that carry a significan...

Modifying the severity and appearance of psoriasis using deep learning to simulate anticipated improvements during treatment.

A neural network was trained to generate synthetic images of severe and moderate psoriatic plaques, after being trained on 375 photographs of patients...

An interpretable machine learning model with demographic variables and dietary patterns for ASCVD identification: from U.S. NHANES 1999-2018.

Current research on the association between demographic variables and dietary patterns with atherosclerotic cardiovascular disease (ASCVD) is limited ...

Use of deep learning-based high-resolution magnetic resonance to identify intracranial and extracranial symptom-related plaques.

This study aims to develop a deep learning model using high-resolution vessel wall imaging (HR-VWI) to differentiate symptom-related intracranial and ...

Artificial intelligence quantification and experienced reader computed tomography analysis for differentiating normal from minimally and mildly diseased coronary arteries: an early real-world compatibility study.

Differentiating normal from minimally and mildly diseased coronary arteries on coronary computed tomographic angiography (CCTA) is crucial, impacting ...

Relationship between atherogenic index of plasma and length of stay in critically ill patients with atherosclerotic cardiovascular disease: a retrospective cohort study and predictive modeling based on machine learning.

BACKGROUND: The atherogenic index of plasma (AIP) is considered an important marker of atherosclerosis and cardiovascular risk. However, its potential...

Deep learning-based LDL-C level prediction and explainable AI interpretation.

This study investigates the use of deep learning (DL) models to predict low-density lipoprotein cholesterol (LDL-C) levels. The dataset obtained from ...

Estimation of Machine Learning-Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular Diseases: UK Biobank Study.

BACKGROUND: The atherosclerotic cardiovascular disease (ASCVD) is associated with dementia. However, the risk factors of dementia in patients with ASC...

Non-invasive classification of non-neoplastic and neoplastic gallbladder polyps based on clinical imaging and ultrasound radiomics features: An interpretable machine learning model.

BACKGROUND: Gallbladder (GB) adenomas, precancerous lesions for gallbladder carcinoma (GBC), lack reliable non-invasive tools for preoperative differe...

Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral Anticoagulant.

Predicting major bleeding in nonvalvular atrial fibrillation (AF) patients on direct oral anticoagulants (DOACs) is crucial for personalized care. Alt...

Detecting severe coronary artery stenosis in T2DM patients with NAFLD using cardiac fat radiomics-based machine learning.

To analyze radiomics features of cardiac adipose tissue in individuals with type 2 diabetes (T2DM) and non-alcoholic fatty liver disease (NAFLD), inte...

Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis.

BACKGROUND: Atherosclerosis is a significant contributor to cardiovascular disease, and conventional diagnostic methods frequently fall short in the t...