Endocrinology

Sex Differences in Diabetes Prevalence, Comorbidities, and Health Care Utilization among American Indians Living in the Northern Plains.

BACKGROUND: The American Indian (AI) population experiences significant diet-related health disparities including diabetes and cardiovascular disease (CVD). Owing to the relatively small sample size of AIs, the population is rarely included in large national surveys such as the NHANES. This exclusion hinders efforts to characterize potentially important differences between AI men and women, track ...

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...

Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning.

PURPOSE: Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease a...

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 st...

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...

Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT.

AIM: To compare the efficacy of computed tomography (CT) texture analysis and conventional evaluation by radiologists for differentiation between larg...

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...

A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound.

OBJECTIVE: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for things such as, cancer detection and differenti...

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...

Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis.

BACKGROUND: The assessment of bone age and skeletal maturity and its comparison to chronological age is an important task in the medical environment f...

Recurrent Saliency Transformation Network for Tiny Target Segmentation in Abdominal CT Scans.

We aim at segmenting a wide variety of organs, including tiny targets (e.g., adrenal gland), and neoplasms (e.g., pancreatic cyst), from abdominal CT ...

Deep-Learning Approach to Automatic Identification of Facial Anomalies in Endocrine Disorders.

BACKGROUND: Deep learning has the potential to assist the medical diagnostic process. We aimed to identify facial anomalies associated with endocrinal...

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...

A curated knowledgebase on endocrine disrupting chemicals and their biological systems-level perturbations.

Human well-being can be affected by exposure to several chemicals in the environment. One such group is endocrine disrupting chemicals (EDCs) that can...

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...

Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists.

BackgroundManagement of thyroid nodules may be inconsistent between different observers and time consuming for radiologists. An artificial intelligenc...