Endocrinology

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

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Deep learning based prediction of depression and anxiety in patients with type 2 diabetes mellitus using regional electronic health records.

BACKGROUND: Depression and anxiety are prevalent mental health conditions among individuals with typ...

Machine learning-driven Raman spectroscopy: A novel approach to lipid profiling in diabetic kidney disease.

Diabetes mellitus is a chronic metabolic disease that increasingly affects people every year. It is ...

Deep learning generalization for diabetic retinopathy staging from fundus images.

. Diabetic retinopathy (DR) is a serious diabetes complication that can lead to vision loss, making ...

AI Machine Learning-Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis.

BACKGROUND: Diabetes is prevalent in older adults, and machine learning algorithms could help predic...

Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision.

INTRODUCTION: Diabetic nephropathy (DN) is a common diabetes-related complication with unclear under...

Interpreting IGF-1 in children treated with recombinant growth hormone: challenges during early puberty.

OBJECTIVE: It can be challenging to determine the correct dosage of recombinant growth hormone (GH) ...

Predictors of glycaemic improvement in children and young adults with type 1 diabetes and very elevated HbA1c using the MiniMed 780G system.

AIMS: This study aimed to identify key factors with the greatest influence on glycaemic outcomes in ...

Perspective: Multiomics and Artificial Intelligence for Personalized Nutritional Management of Diabetes in Patients Undergoing Peritoneal Dialysis.

Managing diabetes in patients on peritoneal dialysis (PD) is challenging due to the combined effects...

PREDICTORS OF TSH NORMALIZATION IN THYROTOXICOSIS PATIENTS AFTER TREATMENT.

CONTEXT: Understanding factors delaying recovery in thyrotoxicosis patients is crucial for optimizin...

Development of a Deep Learning Tool to Support the Assessment of Thyroid Follicular Cell Hypertrophy in the Rat.

Thyroid tissue is sensitive to the effects of endocrine disrupting substances, and this represents a...

ResViT FusionNet Model: An explainable AI-driven approach for automated grading of diabetic retinopathy in retinal images.

BACKGROUND AND OBJECTIVE: Diabetic Retinopathy (DR) is a serious diabetes complication that can caus...

Development of an interpretable machine learning model based on CT radiomics for the prediction of post acute pancreatitis diabetes mellitus.

This study sought to establish and validate an interpretable CT radiomics-based machine learning mod...

Exploring the subtle and novel renal pathological changes in diabetic nephropathy using clustering analysis with deep learning.

To decrease the number of chronic kidney disease (CKD), early diagnosis of diabetic kidney disease i...

Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning.

OBJECTIVE: The artificial pancreas (AP) shows promise for closed-loop glucose control in type 1 diab...

Effects of exogenous insulin supplementation on lipid metabolism in peripartum obese dairy cows.

Cows with high body condition scores experience more severe negative energy balance (NEB) and underg...

E-DFu-Net: An efficient deep convolutional neural network models for Diabetic Foot Ulcer classification.

The Diabetic Foot Ulcer (DFU) is a severe complication that affects approximately 33% of diabetes pa...

Immunometabolic alterations in type 2 diabetes mellitus revealed by single-cell RNA sequencing: insights into subtypes and therapeutic targets.

BACKGROUND: Type 2 Diabetes Mellitus (T2DM) represents a major global health challenge, marked by ch...

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