Latest AI and machine learning research in diabetes for healthcare professionals.
OBJECTIVE: Gestational diabetes mellitus (GDM) is associated with adverse maternal and fetal outcomes. This study aimed to identify early and reliable GDM predictors that would enable implementation of preventive and management measures.
BACKGROUND: Despite excellent prediction performance, noninterpretability has undermined the value of applying deep-learning algorithms in clinical practice. To overcome this limitation, attention mechanism has been introduced to clinical research as an explanatory modeling method. However, potential limitations of using this attractive method have not been clarified to clinical researchers. Furth...
One of the fundamental challenges when dealing with medical imaging datasets is class imbalance. Class imbalance happens where an instance in the clas...
OBJECTIVE: To evaluate diabetic retinopathy (DR) screening via deep learning (DL) and trained human graders (HG) in a longitudinal cohort, as case spe...
BACKGROUND: The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of fa...
BACKGROUND: Anti-oxidants were investigated in several studies as a preventive strategy for prevention of contrast-induced nephropathy (CIN). Omega-3 ...
The diagnosis of diabetic kidney disease can be delayed by limitations of primary biomarkers, which are microalbuminuria and estimated glomerular filt...
Emerging data suggest that type 1 diabetes affects not only the β-cell-containing islets of Langerhans, but also the surrounding exocrine compartment....
We present a fully closed-loop design for an artificial pancreas (AP) that regulates the delivery of insulin for the control of Type I diabetes. Our A...
BACKGROUND: Aberrations in endothelial cells, immune and oxidative pathways are associated with atherosclerosis (ATS) and unstable angina (UA). The ro...
Deep learning has achieved remarkable success in the optical coherence tomography (OCT) image classification task with substantial labelled B-scan ima...
Vascular structures in the retina contain important information for the detection and analysis of ocular diseases, including age-related macular degen...
Machine learning and especially deep learning techniques are dominating medical image and data analysis. This article reviews machine learning approac...
Deep learning methods for diabetic retinopathy (DR) diagnosis are usually criticized as being lack of interpretability in the diagnostic result, thus ...
PURPOSE: To evaluate the performance of a deep learning algorithm in the detection of referral-warranted diabetic retinopathy (RDR) on low-resolution ...
BACKGROUND/OBJECTIVES: The exposure to sucrose in rats has mimic abnormalities attributed to metabolic syndrome (MetS). The effects of honey bee and "...
Proliferative retinopathies, such as diabetic retinopathy and retinopathy of prematurity, are leading causes of vision impairment. A common feature is...
We show that a neural network whose output is obtained as the difference of the outputs of two feedforward networks with exponential activation functi...
BACKGROUND: Diabetic retinopathy and kidney disease share underlying mechanisms of microvascular damage and are often comorbid in people with diabetes...
OBJECTIVE: Diabetes insipidus (DI) can be classified into 2 types: central/neurogenic DI and nephrogenic DI. Most cases of central DI occur after brai...