Endocrinology

Latest AI and machine learning research in endocrinology for healthcare professionals.

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Automated detection of diabetic retinopathy using machine learning classifiers.

OBJECTIVE: Diabetic Retinopathy (DR) is a highly threatening microvascular complication of diabetes ...

Deep learning for intelligent diagnosis in thyroid scintigraphy.

OBJECTIVE: To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid ...

Development of Machine Learning Models for Predicting Postoperative Delayed Remission in Patients With Cushing's Disease.

CONTEXT: Postoperative hypercortisolemia mandates further therapy in patients with Cushing's disease...

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes.

UNLABELLED: Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged ...

[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...

Optical coherence tomography-based diabetic macula edema screening with artificial intelligence.

BACKGROUND: Optical coherence tomography (OCT) is considered as a sensitive and noninvasive tool to ...

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...

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...

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...

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 ...

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...

Identification of Diabetes Risk Factors in Chronic Cardiovascular Patients.

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

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 ...

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...

Artificial intelligence for diabetic retinopathy screening, prediction and management.

PURPOSE OF REVIEW: Diabetic retinopathy is the most common specific complication of diabetes mellitu...

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