Endocrinology

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

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Transfer learning and data augmentation for glucose concentration prediction from colorimetric biosensor images.

A deep learning algorithm is introduced to accurately predict glucose concentrations using colorimet...

Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus.

BACKGROUND: Diabetic kidney disease (DKD) is a common and serious complication of diabetic mellitus ...

Identification of thyroid cancer biomarkers using WGCNA and machine learning.

OBJECTIVE: The incidence of thyroid cancer (TC) is increasing in China, largely due to overdiagnosis...

An effective PO-RSNN and FZCIS based diabetes prediction and stroke analysis in the metaverse environment.

Chronic disease (CD) like diabetes and stroke impacts global healthcare extensively, and continuous ...

A semantic segmentation model for automatic precise identification of pituitary microadenomas with preoperative MRI.

PURPOSE: Magnetic resonance imaging (MRI) is an essential technique for diagnosing pituitary adenoma...

Generating evidence to support the role of AI in diabetic eye screening: considerations from the UK National Screening Committee.

Screening for diabetic retinopathy has been shown to reduce the risk of sight loss in people with di...

Identification of biomarkers related to iron death in diabetic kidney disease based on machine learning algorithms.

BACKGROUND: While ferroptosis has been recognised for its key role in tumour development, its involv...

Integrating deep learning and molecular dynamics simulations for FXR antagonist discovery.

Farnesoid X receptor (FXR) is a key regulator of bile acid, lipid, and glucose homeostasis, making i...

Personalized glucose forecasting for people with type 1 diabetes using large language models.

BACKGROUND AND OBJECTIVE: Type 1 Diabetes (T1D) is an autoimmune disease that requires exogenous ins...

Machine learning fusion for glioma tumor detection.

The early detection of brain tumors is very important for treating them and improving the quality of...

Comparative analysis of deep learning architectures for thyroid eye disease detection using facial photographs.

PURPOSE: To compare two artificial intelligence (AI) models, residual neural networks ResNet-50 and ...

Gender Differences in Predicting Metabolic Syndrome Among Hospital Employees Using Machine Learning Models: A Population-Based Study.

BACKGROUND: Metabolic syndrome (MetS) is a complex condition that captures several markers of dysreg...

An Early Thyroid Screening Model Based on Transformer and Secondary Transfer Learning for Chest and Thyroid CT Images.

IntroductionThyroid cancer is a common malignant tumor, and early diagnosis and timely treatment are...

VisionGuard: enhancing diabetic retinopathy detection with hybrid deep learning.

OBJECTIVES: Early detection of diabetic retinopathy (DR) and timely intervention are critical for pr...

Real-World Type 2 Diabetes Second-Line Treatment Allocation Among Patients.

OBJECTIVE: This study aimed to evaluate the impact of socioeconomic disparities on the allocation of...

Machine learning in lymphocyte and immune biomarker analysis for childhood thyroid diseases in China.

OBJECTIVE: This study aims to characterize and analyze the expression of representative biomarkers l...

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