Dyslipidemia

Machine learning predictive models of LDL-C in the population of eastern India and its comparison with directly measured and calculated LDL-C.

BACKGROUND: LDL-C is a strong risk factor for cardiovascular disorders. The formulas used to calculate LDL-C showed varying performance in different populations. Machine learning models can study complex interactions between the variables and can be used to predict outcomes more accurately. The current study evaluated the predictive performance of three machine learning models-random forests, XGBo...

Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.

Identification of medical conditions using claims data is generally conducted with algorithms based on subject-matter knowledge. However, these claims-based algorithms (CBAs) are highly dependent on the knowledge level and not necessarily optimized for target conditions. We investigated whether machine learning methods can supplement researchers' knowledge of target conditions in building CBAs. Re...

Ultrasound Image Features under Deep Learning in Breast Conservation Surgery for Breast Cancer.

This study was to analyze the effect of the combined application of deep learning technology and ultrasound imaging on the effect of breast-conserving...

Gallstone Disease in Cirrhosis-Pathogenesis and Management.

Gallstones are more common in patients with cirrhosis of the liver, and the incidence increases with severity of liver disease. Pigment stones are the...

Exploring the diagnostic effectiveness for myocardial ischaemia based on CCTA myocardial texture features.

BACKGROUND: To explore the characteristics of myocardial textures on coronary computed tomography angiography (CCTA) images in patients with coronary ...

The effect of consuming different proportions of hummer fish on biochemical and histopathological changes of hyperglycemic rats.

Hammour fish (grouper fish) are known to be of great nutritional value for human consumption, as their protein has a high biological value and contain...

Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review.

BACKGROUND: Artificial intelligence (AI) has served humanity in many applications since its inception. Currently, it dominates the imaging field-in pa...

Multilevel Strip Pooling-Based Convolutional Neural Network for the Classification of Carotid Plaque Echogenicity.

Carotid plaque echogenicity in ultrasound images has been found to be closely correlated with the risk of stroke in atherosclerotic patients. The auto...

Vesseg: An Open-Source Tool for Deep Learning-Based Atherosclerotic Plaque Quantification in Histopathology Images-Brief Report.

Objective: Manual plaque segmentation in microscopy images is a time-consuming process in atherosclerosis research and potentially subject to unaccept...

Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study.

Given the rapid increase in the incidence of cardiometabolic conditions, there is an urgent need for better approaches to prevent as many cases as pos...

Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound.

The automated and accurate carotid plaque segmentation in B-mode ultrasound (US) is an essential part of stroke risk stratification. Previous segmente...

Computed Tomography Angiography under Deep Learning in the Treatment of Atherosclerosis with Rapamycin.

The clinical characteristics and vascular computed tomography (CT) imaging characteristics of patients were explored so as to assist clinicians in dia...

Prophylactic effect of . seed extract on inflammatory markers and histopathological changes in high-fat-fed ovariectomized rats.

BACKGROUND AND AIM: L. seeds (TFG) are used as spices in Indian cuisine. In Indian traditional medicine, TFG is used to treat diabetes, dyslipidemia,...

Deep Learning-Based Carotid Plaque Segmentation from B-Mode Ultrasound Images.

Carotid ultrasound measurement of total plaque area (TPA) provides a method for quantifying carotid plaque burden and monitoring changes in carotid at...

Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model.

DGAT1 plays a crucial controlling role in triglyceride biosynthetic pathways, which makes it an attractive therapeutic target for obesity. Thus, devel...

Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes.

Peripheral artery disease is an atherosclerotic disorder which, when present, portends poor patient outcomes. Low diagnosis rates perpetuate poor mana...

Automated classification of coronary atherosclerotic plaque in optical frequency domain imaging based on deep learning.

BACKGROUND AND AIMS: We developed a deep learning (DL) model for automated atherosclerotic plaque categorization using optical frequency domain imagin...

Tinnitus Frequency is Higher in Patients with Chronic Heart Failure with Reduced Ejection Fraction and is Closely Related to NT-proBNP Level.

There is not enough information about tinnitus and related parameters in patients with heart failure with reduced ejection fraction (HFrEF). In our st...

A deep learning-based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography  images.

PURPOSE: Coronary artery events are mainly associated with atherosclerosis in adult population, which is recognized as accumulation of plaques in arte...

Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4-7th Korea National Health and Nutrition Examination Survey.

Few studies have been conducted to classify and predict the influence of nutritional intake on overweight/obesity, dyslipidemia, hypertension and type...