Diabetes

Predicting metabolic syndrome in Iranian adults using artificial neural networks: a comparative analysis with logistic regression based on demographic, lifestyle, and clinical indicators.

OBJECTIVE: Metabolic syndrome (MetS) is a major risk factor for cardiovascular diseases and type 2 diabetes, imposing a substantial economic and public health burden on Iran's healthcare system. This study aimed to classify the risk of MetS in Iranian adults using an artificial neural network (ANN) and to compare its performance with that of logistic regression based on demographic, lifestyle, and...

From Single Conventional Regression to Ensemble Modeling: Relative Importance of the Healthy Eating Index-2015 Components in Relation to Adverse Pregnancy Outcomes.

The Healthy Eating Index (HEI) is widely used to assess diet quality, but certain contexts (e.g., pregnancy) may benefit from tailored versions. We evaluated whether the HEI's current approach of assigning approximately equal weights to all components to compute the total score is appropriate when studying diet quality around conception. Data were from a U.S. prospective cohort of individuals who ...

Evaluating the Accuracy, Quality, and Reproducibility of AI Chatbot-Generated Diabetes Self-Management Education and Support Plans Aligned With ADCES7.

BACKGROUND: The rapid development of artificial intelligence, particularly large language models (LLMs) such as ChatGPT, Gemini, and Claude, offers ne...

Supervised machine learning identifies impaired mitochondrial quality control in β-cells with development of type 2 diabetes.

Defining molecular pathways driving β-cell failure in type 2 diabetes (T2D) is challenging given donor heterogeneity. We developed an interpretable ma...

Few-shot learning-driven discovery of Lutein suppresses Th1-mediated inflammation via glucose metabolism.

The discovery of selective immunosuppressants for T cell-mediated diseases like Ulcerative Colitis (UC) is a significant challenge. While traditional ...

Network toxicology and bioinformatics analysis predict potential molecular targets and mechanisms by which sevoflurane and propofol influence type 2 diabetes mellitus.

BACKGROUND: Sevoflurane and propofol are commonly used anesthetics that may exert pronounced effects on glucose metabolism and cardiovascular function...

Development and validation of a logistic regression model for predicting visual impairment in middle-aged and older adults with diabetes: results from the China Health and Retirement Longitudinal Study.

AIM: To develop and validate a clinician-friendly logistic regression prediction model for self-reported visual impairment (VI) in middle-aged and old...

Comparative prognostic value of TyG index and TyG-BMI for 90-day all-cause mortality in critically ill older adults with ASCVD: a machine learning analysis of the MIMIC-IV database.

BACKGROUND: The triglyceride-glucose (TyG) index and triglyceride-glucose-body mass index (TyG-BMI) are emerging surrogate markers of insulin resistan...

Predicting cardiovascular death in overweight/obese people with prediabetes using machine learning-a proof-of-concept study.

BACKGROUND: Individuals with prediabetes face an increased risk of cardiovascular (CV) complications, which can ultimately lead to premature mortality...

RDE-DR: robust deep ensemble CNNs for automated diabetic retinopathy detection from fundus images.

Diabetic retinopathy (DR) is a leading cause of preventable blindness, motivating the development of reliable automated screening systems. This work p...

Explainable TabNet for gestational diabetes prediction with physician-in-the-loop and multi-site clinical validation.

BACKGROUND: Gestational diabetes mellitus (GDM) affects 15-25% of pregnancies worldwide and poses serious risks of macrosomia, preeclampsia, neonatal ...

Deep learning strategies for estimating retinal thickness from fundus images: a comparative study with multi-device data.

Color fundus photographs (CFPs) are widely used for diabetic retinopathy (DR) screening due to accessibility. Optical coherence tomography (OCT) provi...

Microaneurysm segmentation under out-of-domain generalization: from diabetic retinopathy to leukemic retinopathy.

PURPOSE: To propose inter-disease out-of-domain generalization (OODG) across retinal diseases for microaneurysm (MA) segmentation using a deep-learnin...

Predicting type II diabetes mellitus in young and middle-aged adults: A machine learning approach using the Utah population database.

AIMS: To develop a machine learning framework for predicting type 2 diabetes mellitus (T2DM) using administrative data and electronic health records (...

Indicators for predicting continuous insulin infusion therapy-related hypokalemia: multicenter retrospective cohort study.

Hypokalemia is a common and potentially life-threatening complication of continuous intravenous insulin infusion (CII) in patients with hyperglycemic ...

AI-Based Digital Pathology-Enabled Spatial-Omics Data Analyses of the Human Kidney.

Identification of tissue-region-specific changes in glycosylation is crucial for understanding the pathogenesis of kidney diseases, yet it remains a g...

Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines.

BACKGROUND: Complication risks in children and adolescents with type 1 diabetes (T1D) can lead to serious health outcomes if not detected early. Despi...

Clinical setting-dependent diagnostic accuracy of artificial intelligence and store-and-forward diabetic retinopathy screening: a systematic review and meta-analysis.

Population-based diabetic retinopathy (DR) screening requires diagnostic strategies that optimize clinical utility by balancing missed disease against...

Structure-aware retinal disentanglement reveals the genetic architecture of ocular and systemic diseases.

Deep learning effectively extracts retinal phenotypes but often functions as an entangled black box, obscuring specific genetic mechanisms and hinderi...

Beyond eGFR and Albuminuria: Biological Pathways and Multiomics in Cardiovascular-Kidney-Metabolic Disease.

Cardiovascular-kidney-metabolic disease (CKM) represents a growing public health challenge driven by the convergence of obesity, diabetes, and cardiov...