Diabetes

Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.

BACKGROUND: Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications, which stays in the top causes of vision impairment and blindness. Therefore, precisely inspecting its progression enables the ophthalmologists to set up appropriate next-visit schedule and cost-effective treatment plans. In the literature, existing work only makes use of numerical attributes in Elect...

Retinal image assessment using bi-level adaptive morphological component analysis.

The automated analysis of retinal images is a widely researched area which can help to diagnose several diseases like diabetic retinopathy in early stages of the disease. More specifically, separation of vessels and lesions is very critical as features of these structures are directly related to the diagnosis and treatment process of diabetic retinopathy. The complexity of the retinal image conten...

Supplementation of OmniGen-AF improves the metabolic response to a glucose tolerance test in beef heifers.

This study determined whether feeding the immunomodulating supplement, OmniGen-AF, to feedlot heifers would alter metabolic profiles to a glucose tole...

Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia.

Hypoglycemia or low blood glucose is the most feared complication of insulin treatment of diabetes. For people with diabetes, the mismatch between the...

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting.

For people with Type 1 diabetes (T1D), forecasting of blood glucose (BG) can be used to effectively avoid hyperglycemia, hypoglycemia and associated c...

Diabetic retinopathy detection through novel tetragonal local octa patterns and extreme learning machines.

Diabetic retinopathy (DR) is an eye disease that victimize the people suffering from diabetes from many years. The severe form of DR results in form o...

Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.

BACKGROUND: Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood glucose (BG) regulation that might result in short and long-term...

Machine Learning Models in Type 2 Diabetes Risk Prediction: Results from a Cross-sectional Retrospective Study in Chinese Adults.

Type 2 diabetes mellitus (T2DM) has become a prevalent health problem in China, especially in urban areas. Early prevention strategies are needed to r...

Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading.

Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the huma...

TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records.

BACKGROUND: Insulin resistance is an early-stage deterioration of Type 2 diabetes. Identification and quantification of insulin resistance requires sp...

Screening and Bioinformatics Analysis of IgA Nephropathy Gene Based on GEO Databases.

PURPOSE: To identify novel biomarkers of IgA nephropathy (IgAN) through bioinformatics analysis and elucidate the possible molecular mechanism.

Analysis of crystallization phenomenon in Indian honey using molecular dynamics simulations and artificial neural network.

Molecular dynamics simulation was performed on sugar profile and moisture content-based mixture systems of six Indian honey samples. Comparative studi...

Development of "Predict ME," an online classifier to aid in differentiating diabetic macular edema from pseudophakic macular edema.

PURPOSE: Differentiating the underlying pathology of macular edema in patients with diabetic retinopathy following cataract surgery can be challenging...

Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network.

Diabetic retinopathy (DR) is the most common cause of blindness in middle-age subjects and low DR screening rates demonstrates the need for an automat...

Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis.

Accurate medical disease diagnosis is considered to be an important classification problem. The main goal of the classification process is to determin...

[Maturity Onset Diabetes of the Young: case report].

BACKGROUND: Maturity Onset Diabetes of the Young (MODY) is a type of diabetes that results from mutations in 13 known genes that play a role in the de...

Minimizing postprandial hypoglycemia in Type 1 diabetes patients using multiple insulin injections and capillary blood glucose self-monitoring with machine learning techniques.

BACKGROUND: Diabetic patients treated with intensive insulin therapies require a tight glycemic control and may benefit from advanced tools to predict...

Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography.

BACKGROUND AND OBJECTIVES: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns betw...

Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.

BACKGROUND: Diabetes mellitus is a chronic disease that impacts an increasing percentage of people each year. Among its comorbidities, diabetics are t...

Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.

We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal...