Diabetes

Machine learning-based prediction of diabetic retinopathy using clinlabomics: a multi-center study.

BACKGROUND: Diabetic retinopathy (DR) is a leading cause of vision loss, yet conventional retinal screening remains costly and resource-intensive. This study developed and validated machine-learning (ML) models using routine laboratory data to provide a cost-effective, accessible alternative for DR risk stratification and triage. METHODS: We analyzed data from 750 patients (363 T2DM; 387 DR) and e...

Cardiovascular Risk Stratification in Youth-Onset Type 2 Diabetes Using Machine Learning.

BACKGROUND: The rising incidence of youth-onset type 2 diabetes mellitus (T2DM), along with the risk of early cardiovascular complications, is concerning. Brachial-ankle pulse wave velocity (baPWV) and carotid intima-media thickness (cIMT) are noninvasive markers of arterial stiffness and atherosclerosis. They serve as important markers for cardiovascular risk assessment. This study aimed to apply...

Tubulointerstitial Diseases: An Updated Framework for Diverse and Emerging Entities.

Tubulointerstitial diseases represent a heterogeneous group of kidney diseases with diverse causes and overlapping histopathologic and immunophenotypi...

Zwitterionic Lipid Nanotherapeutics from Mulberry for Oral Treatment of Diabetic Colitis.

Diabetic colitis is a severe gastrointestinal complication of type 2 diabetes, which presents the key pathophysiological hallmarks of hyperglycemia, i...

Transforming hypoglycemia prediction in adult type 1 diabetes: a systematic review and meta-analysis for precision care.

Type 1 diabetes mellitus (T1DM) patients require lifelong insulin therapy; however, iatrogenic hypoglycemia remains a major clinical challenge, with h...

Machine learning and regional homogeneity reveal early and subtle brain changes in type 1 diabetes.

BACKGROUND AND PURPOSE: Type 1 diabetes mellitus (T1DM) usually begins early in life, and its development impacts brain functioning and cognitive proc...

Comment on "A machine learning approach to predicting severe diabetes distress in adults with type 1 diabetes mellitus".

SPISE index and ensemble machine learning refine cardiovascular risk stratification in stage 0-3 CKM syndrome.

BACKGROUND: While the single-point insulin sensitivity estimator (SPISE) shows promise as an insulin resistance biomarker, its association with cardio...

Molecular basis of precision nutrition: Food components, microbiome-derived metabolites, and multi-omics modeling.

Metabolic disorders, including obesity, type 2 diabetes, metabolic syndrome, and fatty liver disease, reflect multifactorial interactions among diet, ...

Global approval and certification of ophthalmic AI devices: A comparative regulatory perspective.

Artificial intelligence (AI) tools are rapidly reshaping ophthalmology by improving screening and diagnosis for diabetic retinopathy, age-related macu...

Beyond the Mean: A Machine Learning-Based Trend Analysis of CGM Metrics for Improved HbA1C Estimation in Type 2 Diabetes.

BACKGROUND: Hemoglobin A1C (HbA1C) is the gold standard for assessing long-term glycemic control in people with diabetes. Increasing use of continuous...

Deep Learning-based Volition Detection and Action Potential Extraction for Fully Automated Diagnosis of Neuromuscular Disease Using Needle Electromyography Signals.

OBJECTIVE: This study aimed to develop a deep learning-based volition-detection model to automate the diagnostic process and improve neuromuscular dis...

Ubiquitination-driven fibroblast dysfunction: a multi-omics blueprint for precision diagnosis and therapy in diabetic foot ulcer.

A modular deep learning architecture for interpretable disease prediction across tabular clinical and biometric datasets.

Accurate disease prediction using clinical datasets is essential for improving early diagnosis and clinical decision-support systems; however, many ex...

Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs.

Complex survey designs are widely used in medical cohort studies. Developing risk score models that adequately account for the sampling design is esse...

Deep learning-powered quantification of endocrine cells and CD3+ T cells in the natural history of type 1 diabetes.

AIMS/HYPOTHESIS: Histopathological analysis in type 1 diabetes presents challenges in achieving precise characterisation with cellular quantification ...

Financial incentives increase uptake and perceived effectiveness of autonomous medical AI, yet patients still seek human reconfirmation.

Artificial intelligence (AI) is increasingly used for diagnostic screening. In diabetic retinopathy screening, autonomous AI systems can identify dise...

Cumulative stress hyperglycemia ratio exposure and dynamic trajectories reveal prognostic determinants of acute hyperlipidemic pancreatitis: an 5-year cohort study.

BACKGROUND: The stress hyperglycemia ratio (SHR) and glycemic variability (GV) both reflect acute glycemic fluctuations, with established roles in car...

Development and validation of a risk prediction model for dysglycaemia at 6-12 weeks postpartum in Chinese women with gestational diabetes: a retrospective cohort study.

BACKGROUND: Compared with normoglycaemic pregnancies, gestational diabetes mellitus (GDM) confers a markedly elevated risk of dysglycaemia at 6-12 wee...

Prognostic Imaging Biomarkers in Diabetic Macular Edema Treated with Anti-VEGF: A Multicenter AI Perspective.

INTRODUCTION: This study aimed to identify optical coherence tomography (OCT) biomarkers at baseline and after the loading phase (LP) of antivascular ...