Diabetes

Recent Advanced Deep Learning Architectures for Retinal Fluid Segmentation on Optical Coherence Tomography Images.

With non-invasive and high-resolution properties, optical coherence tomography (OCT) has been widely used as a retinal imaging modality for the effective diagnosis of ophthalmic diseases. The retinal fluid is often segmented by medical experts as a pivotal biomarker to assist in the clinical diagnosis of age-related macular diseases, diabetic macular edema, and retinal vein occlusion. In recent ye...

Robot-enhanced diabetes care for middle-aged and older adults living with diabetes in the community: A small sample size mixed-method evaluation.

PURPOSE: This study assessed robot-enhanced healthcare in practical settings for the purpose of community diabetes care.

A Novel Approach for Feature Selection and Classification of Diabetes Mellitus: Machine Learning Methods.

An active research area where the experts from the medical field are trying to envisage the problem with more accuracy is diabetes prediction. Surveys...

Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques.

Continuous noninvasive blood glucose monitoring and estimation management by using photoplethysmography (PPG) technology always have a series of probl...

Fully Automated Abdominal CT Biomarkers for Type 2 Diabetes Using Deep Learning.

Background CT biomarkers both inside and outside the pancreas can potentially be used to diagnose type 2 diabetes mellitus. Previous studies on this t...

Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review.

BACKGROUND: Major chronic diseases such as cardiovascular disease (CVD), diabetes, and cancer impose a significant burden on people and health care sy...

Detection of signs of disease in external photographs of the eyes via deep learning.

Retinal fundus photographs can be used to detect a range of retinal conditions. Here we show that deep-learning models trained instead on external pho...

Deep learning approach identified a gene signature predictive of the severity of renal damage caused by chronic cadmium accumulation.

Epidemiology studies have indicated that environmental cadmium exposure, even at low levels, will result in chronic cadmium accumulation in the kidney...

Quantitative analysis of Raman spectra for glucose concentration in human blood using Gramian angular field and convolutional neural network.

In this study, convolutional neural network based on Gramian angular field (GAF-CNN) was firstly proposed. The 1-D Raman spectral data was converted i...

Fully Convolutional Neural Network Deep Learning Model Fully in Patients with Type 2 Diabetes Complicated with Peripheral Neuropathy by High-Frequency Ultrasound Image.

This study was aimed at exploring the diagnostic value of high-frequency ultrasound imaging based on a fully convolutional neural network (FCN) for pe...

Evaluation of Nano-curcumin effects against Tartrazine-induced abnormalities in liver and kidney histology and other biochemical parameters.

In the current study, 40 albino male rats were investigated to evaluate the impact of Nano-curcumin (Nano-CUR) administration against Tartrazine (TZ)-...

Development of Various Diabetes Prediction Models Using Machine Learning Techniques.

BACKGROUND: There are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create v...

Intelligent Algorithm-Based Ultrasound Image for Evaluating the Effect of Comprehensive Nursing Scheme on Patients with Diabetic Kidney Disease.

This study was aimed at exploring the effect of ultrasound image evaluation of comprehensive nursing scheme based on artificial intelligence algorithm...

Cross-Camera External Validation for Artificial Intelligence Software in Diagnosis of Diabetic Retinopathy.

AIMS: To investigate the applicability of deep learning image assessment software VeriSee DR to different color fundus cameras for the screening of di...

Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study.

BACKGROUND: Diabetic retinopathy is a leading cause of preventable blindness, especially in low-income and middle-income countries (LMICs). Deep-learn...

Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm.

BACKGROUND: Type 2 diabetes is a major health concern worldwide. The present study is aimed at discovering effective biomarkers for an efficient diagn...

Deep Learning Algorithm-Based MRI Image in the Diagnosis of Diabetic Macular Edema.

This study investigates the value of magnetic resonance imaging (MRI) based on a deep learning algorithm in the diagnosis of diabetic macular edema (D...

Evaluation of the Antidiabetic Potential of an Isolated Hydroalcoholic Fraction from the Fruit of .

The hydro-alcoholic extract of fruits was investigated for preliminary phytochemical screening and characterized by high-performance thin-layer chrom...

Predicting age at onset of type 1 diabetes in children using regression, artificial neural network and Random Forest: A case study in Saudi Arabia.

The rising incidence of type 1 diabetes (T1D) among children is an increasing concern globally. A reliable estimate of the age at onset of T1D in chil...

Application of Improved U-Net Convolutional Neural Network for Automatic Quantification of the Foveal Avascular Zone in Diabetic Macular Ischemia.

OBJECTIVES: The foveal avascular zone (FAZ) is a biomarker for quantifying diabetic macular ischemia (DMI), to automate the identification and quantif...