Diabetes

DMENet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs.

UNLABELLED: Diabetic Macular Edema (DME) is an advanced stage of Diabetic Retinopathy (DR) and can lead to permanent vision loss. Currently, it affects 26.7 million people globally and on account of such a huge number of DME cases and the limited number of ophthalmologists, it is desirable to automate the diagnosis process. Computer-assisted, deep learning based diagnosis could help in early detec...

An Artificial Neural Network-based Predictive Model to Support Optimization of Inpatient Glycemic Control.

Achieving glycemic control in critical care patients is of paramount importance, and has been linked to reductions in mortality, intensive care unit (ICU) length of stay, and morbidities such as infection. The myriad of illnesses and patient conditions render maintenance of glycemic control very challenging in this setting. This study involved collection of continuous glucose monitoring (CGM) da...

Predicting 10-Year Risk of End-Organ Complications of Type 2 Diabetes With and Without Metabolic Surgery: A Machine Learning Approach.

OBJECTIVE: To construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients ...

Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management.

PURPOSE OF REVIEW: Machine learning (ML) is increasingly being studied for the screening, diagnosis, and management of diabetes and its complications....

The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method.

Recently, combining feature extraction and classification method of electroencephalogram (EEG) signals has been widely used in identifying mild cognit...

Automatic detection of rare pathologies in fundus photographs using few-shot learning.

In the last decades, large datasets of fundus photographs have been collected in diabetic retinopathy (DR) screening networks. Through deep learning, ...

Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs.

PURPOSE: To develop a deep learning approach based on deep residual neural network (ResNet101) for the automated detection of glaucomatous optic neuro...

Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals.

Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remai...

Learning Personalized Treatment Rules from Electronic Health Records Using Topic Modeling Feature Extraction.

To address substantial heterogeneity in patient response to treatment of chronic disorders and achieve the promise of precision medicine, individualiz...

Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review.

There is growing interest in the potential of artificial intelligence to support decision-making in health and social care settings. There is, however...

A Deep Neural Network Application for Improved Prediction of [Formula: see text] in Type 1 Diabetes.

[Formula: see text] is a primary marker of long-term average blood glucose, which is an essential measure of successful control in type 1 diabetes. Pr...

Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and meta-analysis.

BACKGROUND: Diabetic retinopathy (DR) is one of the leading causes of blindness globally. Earlier detection and timely treatment of DR are desirable t...

Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning.

PURPOSE: To automatically detect and classify the lesions of diabetic retinopathy (DR) in fundus fluorescein angiography (FFA) images using deep learn...

Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model.

AIMS: Identification, a priori, of those at high risk of progression from pre-diabetes to diabetes may enable targeted delivery of interventional prog...

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG.

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces th...

Machine learning as new promising technique for selection of significant features in obese women with type 2 diabetes.

Background The global trend of obesity and diabetes is considerable. Recently, the early diagnosis and accurate prediction of type 2 diabetes mellitus...

The sweet detection of rolling circle amplification: Glucose-based electrochemical genosensor for the detection of viral nucleic acid.

Herein, an isothermal padlock probe-based assay for the simple and portable detection of pathogens coupled with a glucose oxidase (GOx)-based electroc...

Insulin Resistance Associated with Plasma Xanthine Oxidoreductase Activity Independent of Visceral Adiposity and Adiponectin Level: MedCity21 Health Examination Registry.

BACKGROUND: Higher levels of uric acid production have been reported in individuals with visceral fat obesity, and obesity is known to enhance xanthin...

Membranous Nephropathy with Rapid Progression.

We report a 49-year-old man with microscopic hematuria, subnephrotic proteinuria, and rapidly progressive renal failure. His biopsy had features of Ph...

Effects of post-weaning supplementation of immunomodulatory feed ingredient on body weight and cortisol concentrations in program-fed beef heifers.

The objective of this study was to determine the effects of an immunomodulatory feed ingredient during post-weaning on growth and cortisol concentrati...