Endocrinology

Twelve patients with mental illness who complained of postprandial symptoms in addition to fatigue showed central adrenal insufficiency.

BACKGROUND: Adrenal insufficiency (AI) may cause psychiatric symptoms. We evaluated the correlation between the hypothalamic-pituitary-adrenal axis (HPA) function in patients with mental illness who complained of postprandial symptoms in addition to fatigue.

Predicting youth diabetes risk using NHANES data and machine learning.

Prediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performan...

Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4-7th Korea National Health and Nutrition Examination Survey.

Few studies have been conducted to classify and predict the influence of nutritional intake on overweight/obesity, dyslipidemia, hypertension and type...

Role of Regulatory Non-Coding RNAs in Aggressive Thyroid Cancer: Prospective Applications of Neural Network Analysis.

Thyroid cancer (TC) is the most common endocrine malignancy. Most TCs have a favorable prognosis, whereas anaplastic thyroid carcinoma (ATC) is a leth...

Automatic Detection of Thyroid and Adrenal Incidentals Using Radiology Reports and Deep Learning.

BACKGROUND: Computed tomography (CT) is commonly performed when evaluating trauma patients with up to 55% showing incidental findings. Current workflo...

Predicting nodal metastases in papillary thyroid carcinoma using artificial intelligence.

BACKGROUND: The presence of nodal metastases is important in the treatment of papillary thyroid carcinoma (PTC). We present our experience using a con...

An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from , Decreases HbA1c in a Prediabetic Population.

The prevalence of prediabetes is rapidly increasing, and this can lead to an increased risk for individuals to develop type 2 diabetes and associated ...

Microparticle-Based Biochemical Sensing Using Optical Coherence Tomography and Deep Learning.

Advancing continuous health monitoring beyond vital signs to biochemistry will revolutionize personalized medicine. Herein, we report a biosensing pla...

A Zwitterionic-Aromatic Motif-Based ionic skin for highly biocompatible and Glucose-Responsive sensor.

Electronic skins that can sense external stimuli have been of great significance in artificial intelligence and smart wearable devices in recent years...

Understanding inherent image features in CNN-based assessment of diabetic retinopathy.

Diabetic retinopathy (DR) is a leading cause of blindness and affects millions of people throughout the world. Early detection and timely checkups are...

Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning.

INTRODUCTION: Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study ...

Efficient Automated Disease Diagnosis Using Machine Learning Models.

Recently, many researchers have designed various automated diagnosis models using various supervised learning models. An early diagnosis of disease ma...

Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes.

IMPORTANCE: Systems-level barriers to diabetes care could be improved with population health planning tools that accurately discriminate between high-...

Prediction of brain age from routine T2-weighted spin-echo brain magnetic resonance images with a deep convolutional neural network.

Our study investigated the feasibility and clinical relevance of brain age prediction using axial T2-weighted images (T2-WIs) with a deep convolutiona...

Machine-learning based prediction of Cushing's syndrome in dogs attending UK primary-care veterinary practice.

Cushing's syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing's syndrome can be a c...

Using Graph Representation Learning to Predict Salivary Cortisol Levels in Pancreatic Cancer Patients.

Cortisol is a glucocorticoid hormone that is critical to immune system functioning. Studies show that prolonged exposure to high levels of cortisol ca...

Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology.

In this study, we propose a technique for diagnosing both type 1 and type 2 diabetes in a quick, noninvasive way by using equipment that is easy to tr...

Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors.

This paper aims to identify structural motifs within a molecule that contribute the most toward a chemical being an endocrine disruptor. We have devel...

Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods.

BACKGROUND: Genotype-phenotype predictions are of great importance in genetics. These predictions can help to find genetic mutations causing variation...

Intelligent Diagnosis of Thyroid Ultrasound Imaging Using an Ensemble of Deep Learning Methods.

: At present, thyroid disorders have a great incidence in the worldwide population, so the development of alternative methods for improving the diagno...