Endocrinology

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

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Leveraging AlphaFold models to predict androgenic effects of endocrine-disrupting chemicals through zebrafish androgen receptor analysis.

The androgen receptor (AR) activation by androgens is vital for tissue development, sexual different...

A Novel Mouse Model of Type 2 Diabetes Using a Medium-Fat Diet, Fructose, and Streptozotocin to Study the Complications of Human Disease.

The study of type 2 diabetes mellitus (T2DM) pathophysiology relies mainly on the use of animal mode...

Effects of Dietary Gamma-Aminobutyric Acid (GABA) Inclusion on Acute Temperature Stress Responses in Juvenile Olive Flounder ().

This study investigated the potential of dietary gamma-aminobutyric acid (GABA) inclusion to mitigat...

Mapping variants in thyroid hormone transporter MCT8 to disease severity by genomic, phenotypic, functional, structural and deep learning integration.

Predicting and quantifying phenotypic consequences of genetic variants in rare disorders is a major ...

Early prediction of cardiovascular events following treatments in female breast cancer patients: Application of real-world data and artificial intelligence.

• Application of real-world data and artificial intelligence in detecting cardiotoxicity following c...

Post-Bariatric Hypoglycemia After Gastric Bypass: Clinical Characteristics, Risk Factors, and Future Directions-A Response to Grover et al.

BACKGROUND: Post-bariatric hypoglycemia (PBH) after Roux-en-Y gastric bypass (RYGB) is a complex com...

An explainable non-invasive hybrid machine learning framework for accurate prediction of thyroid-stimulating hormone levels.

Machine learning models, including thyroid biomarkers, are increasingly utilized in healthcare for b...

Harnessing Artificial Intelligence for Precision Diagnosis and Treatment of Triple Negative Breast Cancer.

Triple-Negative Breast Cancer (TNBC) is a highly aggressive subtype of breast cancer (BC) characteri...

Transcriptome analysis reveals the potential role of neural factor EN1 for long-terms survival in estrogen receptor-independent breast cancer.

Breast cancer patients with estrogen receptor-negative (ERneg) status, encompassing triple negative ...

Diagnostic value of deep learning of multimodal imaging of thyroid for TI-RADS category 3-5 classification.

BACKGROUND: Thyroid nodules classified within the Thyroid Imaging Reporting and Data Systems (TI-RAD...

Machine Learning Models Integrating Dietary Indicators Improve the Prediction of Progression from Prediabetes to Type 2 Diabetes Mellitus.

: Diet plays an important role in preventing and managing the progression from prediabetes to type 2...

Advanced pathological subtype classification of thyroid cancer using efficientNetB0.

BACKGROUND: Thyroid cancer is a prevalent malignancy requiring accurate subtype identification for e...

Early prediction of postpartum dyslipidemia in gestational diabetes using machine learning models.

This study addresses a gap in research on predictive models for postpartum dyslipidemia in women wit...

Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease.

Diabetic kidney disease (DKD) is a serious healthcare dilemma. Nonetheless, the interplay between th...

A deep learning approach to understanding controlled ovarian stimulation and in vitro fertilization dynamics.

Infertility, recognized by the World Health Organization (WHO) as a disease affecting the male or fe...

D-GET: Group-Enhanced Transformer for Diabetic Retinopathy Severity Classification in Fundus Fluorescein Angiography.

Early detection of Diabetic Retinopathy (DR) is vital for preserving vision and preventing deteriora...

Comparison of the accuracy of GPT-4 and resident physicians in differentiating benign and malignant thyroid nodules.

OBJECTIVE: To assess the diagnostic performance of the GPT-4 model in comparison to resident physici...

An early prediction model for gestational diabetes mellitus created using machine learning algorithms.

OBJECTIVE: To investigate high-risk factors for gestational diabetes mellitus (GDM) in early pregnan...

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