Endocrinology

[Determination of four bisphenol environmental hormone residues in infant serum by liquid chromatography-tandem mass spectrometry].

Bisphenols are important industrial raw materials that are widely used to produce plastic bottles (f...

Smartphone-based diabetic macula edema screening with an offline artificial intelligence.

BACKGROUND: Diabetic macular edema (DME) is a sight-threatening condition that needs regular examina...

[High-efficiency separation and analysis of monosaccharides in Pueraria polysaccharides by pressurized capillary electrochromatography].

Pueraria polysaccharides have been proven to possess biological activities such as bacteriostasis, a...

Fennel fortified diet: New perspective with regard to fertility and sex hormones.

The objective of this study was to evaluate the effect of Foeniculum vulgare (FV) on fertility of mi...

Feasibility of machine learning based predictive modelling of postoperative hyponatremia after pituitary surgery.

PURPOSE: Hyponatremia after pituitary surgery is a frequent finding with potential severe complicati...

Nodule Localization in Thyroid Ultrasound Images with a Joint-Training Convolutional Neural Network.

The accurate localization of nodules in ultrasound images can convey crucial information to support ...

Ultrasonographic Thyroid Nodule Classification Using a Deep Convolutional Neural Network with Surgical Pathology.

Ultrasonography with fine-needle aspiration biopsy is commonly used to detect thyroid cancer. Howeve...

Use of artificial intelligence and machine learning for estimating malignancy risk of thyroid nodules.

PURPOSE OF REVIEW: Current methods for thyroid nodule risk stratification are subjective, and artifi...

Cost-effectiveness of Autonomous Point-of-Care Diabetic Retinopathy Screening for Pediatric Patients With Diabetes.

IMPORTANCE: Screening for diabetic retinopathy is recommended for children with type 1 diabetes (T1D...

Identification of Diabetes Risk Factors in Chronic Cardiovascular Patients.

Specific predictive models for diabetes polyneuropathy based on screening methods, for example Nerve...

Rosiglitazone-induced changes in the oxidative stress metabolism and fatty acid composition in relation with trace element status in the primary adipocytes.

BACKGROUND: Metabolic syndrome, obesity and type 2 diabetes are metabolic disorders characterized by...

Deep Convolutional Neural Networks for Thyroid Tumor Grading using Ultrasound B-mode Images.

The performances of deep convolutional neural network (DCNN) modeling and transfer learning (TF) for...

Assessment and prediction of restless leg syndrome (RLS) in patients with diabetes mellitus type II through artificial intelligence (AI).

This study aimed to diagnose the incidence of restless leg syndrome (RLS) in patients with diabetes ...

Artificial Intelligence Uncovered Clinical Factors for Cardiovascular Events in Myocardial Infarction Patients with Glucose Intolerance.

PURPOSE: Glucose intolerance (GI), defined as either prediabetes or diabetes, promotes cardiovascula...

Unsupervised Machine Learning for the Discovery of Latent Clusters in COVID-19 Patients Using Electronic Health Records.

The goal of this paper was to apply unsupervised machine learning techniques towards the discovery o...

Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions.

PURPOSE: To provide an overview of fundamental concepts in machine learning (ML), review the literat...

Diagnostic classification of solitary pulmonary nodules using support vector machine model based on 2-[18F]fluoro-2-deoxy-D-glucose PET/computed tomography texture features.

PURPOSE: This study aimed to evaluate the diagnostic value of a support vector machine (SVM) model b...

Machine learning-based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming?

OBJECTIVE: Machine learning (ML) is an innovative method to analyze large and complex data sets. The...

Editorial. Machine learning and artificial intelligence applied to the diagnosis and management of Cushing disease.

Performance of deep neural network-based artificial intelligence method in diabetic retinopathy screening: a systematic review and meta-analysis of diagnostic test accuracy.

OBJECTIVE: Automatic diabetic retinopathy screening system based on neural networks has been used to...

Thyroid Nodule Malignancy Risk Stratification Using a Convolutional Neural Network.

This study evaluates the performance of convolutional neural networks (CNNs) in risk stratifying the...