Diabetes

[Digital transformation in endocrinology/diabetology, pneumology, and hematology/oncology : Where do we stand today-and what lies ahead?].

As in many areas of medicine, increasing digitalisation is also having an impact on everyday practice in internal medicine, entailing both enormous potential and major challenges. As an interdisciplinary field, usually in cooperation with pneumology, diabetology/endocrinology and haematology/oncology, important questions arise regarding optimal use of digital technologies. These can be of great va...

Evaluating Large Language Models-Generated Health Education Materials for Discharged Patients with Diabetes: A Comparative Analysis.

BACKGROUND: Diabetes is a chronic condition requiring long-term management, and continuous health education is vital for improving disease awareness and self-management. Large language models (LLMs), advanced artificial intelligence systems trained on large text data sets, have shown promise in generating diabetes-related educational materials. While LLMs can generate accurate and readable content...

A nine-gene diagnostic model for IgA nephropathy based on multi-cohort machine learning: integrating gene expression and immunohistochemical validation.

IgA nephropathy (IgAN) is the most common primary glomerulonephritis, requiring improved diagnostic tools. We analyzed three cohorts (GSE37460, GSE937...

HOG-CNN: Integrating Histogram of Oriented Gradients with Convolutional Neural Networks for Retinal Image Classification.

The analysis of fundus images is critical for the early detection and diagnosis of retinal diseases such as diabetic retinopathy (DR), glaucoma, and a...

Comprehensive evaluation of triglyceride glucose index-a body shape index (TyG-ABSI) for incident peripheral artery disease: data-driven phenotyping and machine learning-based risk prediction in the UK Biobank.

BACKGROUND: The prevalence of Peripheral Artery Disease (PAD) is rising globally, yet early risk stratification remains challenging due to the limitat...

Identifying Predictors of Early Treatment Intensification in Individuals With Type 2 Diabetes Treated With GLP-1 Receptor Agonists: A Machine Learning-Based Analysis of a Large Real World Cohort.

AIMS: Despite the proven efficacy of GLP-1 receptor agonists (GLP-1 RAs), many patients with type 2 diabetes (T2DM) are not able to achieve glycaemic ...

Training and validation of an automated algorithm to differentiate no and minimal diabetic retinopathy from more severe stages in wide-field images.

PURPOSE: Diabetic retinopathy (DR) is a leading cause of blindness in the working-age population. Screening is essential to identify and treat sight-t...

LRF-UNet: Low-Rank Factorized Convolution Deep-Learning Networks for Visceral Adipose and Muscle Tissue Segmentation in Abdominal Computed Tomography Image.

Quantitative analysis of skeletal muscle (SM) and visceral adipose tissue (VAT) cross-sectional volumes at the third lumbar vertebral level (L3) on ab...

AI-Enabled Personalization of Semaglutide Therapy in Type 2 Diabetes: Systematic Review With an Integration Framework.

BACKGROUND: Type 2 diabetes mellitus (T2D) is a rapidly growing global health concern requiring innovative treatment methods. Ozempic (semaglutide), a...

An Accurate Glucose Detection Platform Using Colorimetry and Supervised Learning Algorithms.

Maintaining optimal health and preventing diabetes-related complications require accurate and timely monitoring of blood glucose levels. In this conte...

BigEye: a clinically interpretable deep learning framework for diabetic retinopathy detection and stage prediction.

Diabetic Retinopathy (DR) is a major cause of vision loss and blindness in diabetic individuals. DR is conventionally diagnosed by assessing retinal l...

AI-Driven Diagnostics vs. Clinician Assessment in Diabetic Retinopathy: A Comparative Analysis at a Secondary Eye Care Centre.

PURPOSE: To determine the diagnostic accuracy and reliability of artificial intelligence (AI) in identifying diabetic retinopathy (DR) and macular oed...

Artificial Intelligence in Screening and Grading Diabetic Eye Diseases: A Systematic Review From Algorithms to Clinic.

SIGNIFICANCE: This systematic review comprehensively synthesises the progress of artificial intelligence in the grading diagnosis of diabetes-related ...

Promoting clinical trials in pediatric IgA nephropathy through extrapolation: an artificial intelligence-enhanced literature review of the disease in pediatrics sponsored by the Kidney Health Initiative.

BACKGROUND: There is an urgent need to evaluate the efficacy of novel therapeutics that have been approved for use in adults with IgA nephropathy (IgA...

Characterization of biomarker profiles in patients with coronary artery disease: A prospective coronary computed tomography angiography study.

BACKGROUND AND AIMS: Conventional biomarkers such as low-density lipoprotein (LDL) and high-density lipoprotein may fail to identify patients' risk fo...

Integrating the Glycemia Risk Index Into Clinical Practice and Research: A Consensus Report.

A panel of experts in the use of continuous glucose monitoring (CGM) data in the treatment of diabetes met in Burlingame, California on October 27, 20...

Association of stress hyperglycemia ratio with the incidence of delirium in critically ill patients: a retrospective cohort study with exploratory machine-learning analyses.

Preoperative Risk Score for Mortality Within 3 Years of Elective Aortobifemoral Artery Bypass.

BACKGROUND: The purpose of this study was to create a risk score for mortality within 3 years of elective aortobifemoral artery bypass for aortoiliac ...

Screening, validation, and transcriptional regulation analysis of oxidative stress-related biomarkers in gestational diabetes mellitus: SH3BP5, ITGAM, PRRG1, and MIS12.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common pregnancy complication linked to adverse outcomes, highlighting the need for new diagnosti...

Modifiable risk factors associated with increased retinal age gap in an Australian population.

Retinal age gap (RAG)-the difference between retina-predicted age and chronological age-indicates biological ageing that has been linked to the risk o...