Endocrinology

Classifying retinal images via vascular-optic disc cross-segmentation and attentive feature selection.

The prevalence of retinal disorders is rising at an alarming rate, posing a significant risk of irreversible blindness without timely intervention. Recent advancements in artificial intelligence (AI) have enabled the development of automated screening systems for retinal image analysis. However, building robust models presents challenges due to inconsistencies in image acquisition, varying resolut...

Stress hyperglycemia ratio improves machine learning-based mortality risk prediction in critically ill COVID-19 patients: a multicenter retrospective study.

Predicting the diabetic foot in patients with type 2 diabetes mellitus based on machine learning.

BACKGROUND: Diabetic foot (DF) is a severe complication of type 2 diabetes mellitus (T2DM), contributing to significant morbidity and healthcare costs...

Properties of AgNPs stabilized with polyvinylpyrrolidone relevant to antidiabetic agents.

Type 2 diabetes mellitus (DM2) is a chronic metabolic disease. Silver nanoparticles (AgNPs) show promise in their treatment. This study assessed the p...

Pediatric diabetes prediction using machine learning.

Diabetes is a chronic condition that affects a substantial portion of the global population and is linked to elevated mortality rates and a range of s...

AI-Enhanced Imaging for Diabetic Foot Ulcer Risk Assessment and Diagnosis: A Retrospective Cohort Study.

BACKGROUND: The automated assessment and prediction of diabetic foot ulcer (DFU) severity depends heavily on precise segmentation of the ulcer region....

Serial Aqueous Humor Proteomics in Diabetic Macular Edema: Correlation of Novel Targets with Retinal Fluid Volume.

OBJECTIVE: Diabetic macular edema (DME) with incomplete response to anti-VEGF therapy is a significant challenge. Here, we integrated advanced proteom...

PODiaCarD: a prototype of a digital twin platform for the management of pediatric obesity and related cardiometabolic complications.

UNLABELLED: Childhood obesity is the main driver of early metabolic risk, predisposing to cardiovascular disease (CVD) and type 2 diabetes (T2D), whic...

Machine Learning Insights into Age-Related Sex Differences in Blood Pressure: Influence of Diabetes and Body Mass Index.

INTRODUCTION: The age-related dynamics of blood pressure (BP) arise from complex, often concurrent interactions among multiple factors (e.g., sex, typ...

Systematic Review on Deep Learning Algorithms for Blood Glucose Forecasting in Type 1 Diabetes.

Type 1 Diabetes (T1D) is a chronic metabolic disease characterized by elevated blood glucose (BG) concentrations, resulting from the immune-mediated d...

Impact of fine-grained learning rate configuration on the performance of medical image segmentation models.

Research on deep learning for medical image segmentation has shifted from single-modality networks to multimodal data fusion. Updating the parameters ...

Multi-center multi-omics integration predicts individualized prognosis in medullary thyroid carcinoma.

Medullary thyroid carcinoma (MTC) is a rare, aggressive neuroendocrine tumor with limited treatment options and frequent recurrence. Comprehensive rec...

Identification of modifiable plasma protein markers of cardiometabolic risk in children and adolescents with obesity.

Pediatric obesity is linked to multi-organ inflammation and an increased risk of cardiometabolic and steatotic liver disease. To identify circulating ...

Assessment of Correlation Between Diabetic Retinopathy and Metabolic Biomarkers Using Artificial Intelligence.

BACKGROUND: One of the main causes of blindness in the world, diabetic retinopathy (DR) is a dangerous condition that impairs vision in diabetics. Pre...

SERS-based deep learning approach for early detection of gestational diabetes mellitus.

Early and precise diagnosis of gestational diabetes mellitus (GDM) is crucial for improving maternal and neonatal outcomes and reducing the risk of ad...

Quality of Life and Decision Regret Risk in Graves' Disease After Radioactive Iodine Therapy: A Prospective Cohort Study.

OBJECTIVE: To identify pre-treatment determinants of hypothyroidism and decision regret (DR) following radioiodine (RAI) therapy in Graves' disease (G...

Non-invasive Prediction of CYP11B2-Defined Subtypes in Primary Aldosteronism Using 18F-Pentixafor PET/CT and Machine Learning.

PURPOSE: This study aims to develop and validate an interpretable machine learning model that integrates clinical data, radiomics, and deep learning (...

Risk Factors and an Interpretable Machine Learning Model for Predicting Spinal Epidural Lipomatosis: A Multicenter Study.

STUDY DESIGN: A retrospective multicenter study. OBJECTIVE: To identify independent risk factors for spinal epidural lipomatosis (SEL) and to develop ...

A research on applying the diffusion model algorithm for Infrared and Raman spectroscopy data augmentation to improve the accuracy of diseases.

In recent years, Raman and Infrared spectroscopy have become important tools in disease diagnosis due to their high sensitivity and non-invasive detec...

Predicting postpartum glucose intolerance in women with gestational diabetes mellitus in primary care: A machine learning approach using XGBoost and SHAP values.

OBJECTIVE(S): To develop and internally validate an interpretable machine learning model using eXtreme Gradient Boosting (XGBoost) and Shapley Additiv...