Endocrinology

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

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Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals.

Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and i...

User-Centered Prototype Design of a Health Care Robot for Treating Type 2 Diabetes in the Community Pharmacy: Development and Usability Study.

BACKGROUND: Technology can be an effective tool for providing health services and disease self-manag...

Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms.

OBJECTIVES: This study aimed to identify risk factors for diabetic retinopathy (DR) and develop mach...

Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis.

OBJECTIVE: To systematically review and meta-analyze the effectiveness of deep learning algorithms a...

Preoperative Assessment of Ki-67 Labeling Index in Pituitary Adenomas Using Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI.

BACKGROUND: Ki-67 labeling index (Ki-67 LI) is a proliferation marker that is correlated with aggres...

Dynamic glucose enhanced imaging using direct water saturation.

PURPOSE: Dynamic glucose enhanced (DGE) MRI studies employ CEST or spin lock (CESL) to study glucose...

Tlalpan 2020 Case Study: Enhancing Uric Acid Level Prediction with Machine Learning Regression and Cross-Feature Selection.

Uric acid is a key metabolic byproduct of purine degradation and plays a dual role in human health....

Interpretable machine learning for thyroid cancer recurrence predicton: Leveraging XGBoost and SHAP analysis.

PURPOSE: For patients suffering from differentiated thyroid cancer (DTC), several clinical, laborato...

TEDML: a new machine learning (ML) approach for predicting thyroid eye disease and identifying key biomarkers.

Thyroid eye disease (TED) features immune infiltration and metabolic dysregulation. Understanding th...

Multitarget Natural Compounds for Ischemic Stroke Treatment: Integration of Deep Learning Prediction and Experimental Validation.

Ischemic stroke's complex pathophysiology demands therapeutic approaches targeting multiple pathways...

Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms.

In recent years, diabetes has become a global public health problem, and it is reported that the mig...

Biological age prediction using a DNN model based on pathways of steroidogenesis.

Aging involves the progressive accumulation of cellular damage, leading to systemic decline and age-...

Developing a machine learning-based predictive model for levothyroxine dosage estimation in hypothyroid patients: a retrospective study.

Hypothyroidism, a common endocrine disorder, has a high incidence in women and increases with age. L...

Machine learning based model for the early detection of Gestational Diabetes Mellitus.

BACKGROUND: Gestational Diabetes Mellitus (GDM) is one of the most common medical complications duri...

A multi model deep net with an explainable AI based framework for diabetic retinopathy segmentation and classification.

Diabetic Retinopathy (DR) is a serious condition affecting diabetes people caused by hemorrhage in t...

ELTIRADS framework for thyroid nodule classification integrating elastography, TIRADS, and radiomics with interpretable machine learning.

Early detection of malignant thyroid nodules is crucial for effective treatment, but traditional dia...

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