Diabetes

SEM model analysis of diabetic patients' acceptance of artificial intelligence for diabetic retinopathy.

AIMS: This study aimed to investigate diabetic patients' acceptance of artificial intelligence (AI) ...

Predicting isolated impaired glucose tolerance without oral glucose tolerance test using machine learning in Chinese Han men.

BACKGROUND: Isolated Impaired Glucose Tolerance (I-IGT) represents a specific prediabetic state that...

GDM-BC: Non-invasive body composition dataset for intelligent prediction of Gestational Diabetes Mellitus.

Gestational Diabetes Mellitus (GDM) refers to any degree of impaired glucose tolerance with onset or...

Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study.

BACKGROUND: Retinopathy of prematurity (ROP) is the leading preventable cause of childhood blindness...

Optimizing diabetic retinopathy detection with electric fish algorithm and bilinear convolutional networks.

Diabetic Retinopathy (DR) is a leading cause of vision impairment globally, necessitating regular sc...

AI-based phenotyping of hepatic fiber morphology to inform molecular alterations in metabolic dysfunction-associated steatotic liver disease.

BACKGROUND AND AIMS: Hepatic fiber morphology may significantly enhance our understanding of molecul...

The association of trimethylamine N-oxide with diabetic retinopathy Pathology: Insights from network toxicology and molecular docking analysis.

Trimethylamine N-oxide (TMAO), a gut microbiota-derived metabolite, has emerged as a potential contr...

A novel deep learning approach to classify 3D foot types of diabetic patients.

Diabetes mellitus is a worldwide epidemic that leads to significant changes in foot shape, deformiti...

Diabetic retinopathy detection based on mobile maxout network and weber local descriptor feature selection using retinal fundus image.

Retinal screening provides for earlier detection of diabetic retinopathy (DR) as well as prompt diag...

Comparison of artificial intelligence-generated and physician-generated patient education materials on early diabetic kidney disease.

BACKGROUND: Diabetic kidney disease (DKD) is a common and serious complication of diabetes mellitus ...

Computational modelling for risk assessment of neurological disorder in diabetes using Hodgkin-Huxley model.

BACKGROUND: Diabetes mellitus, characterized by chronic glucose dysregulation, significantly increas...

Sharper insights: Adaptive ellipse-template for robust fovea localization in challenging retinal landscapes.

Automated identification of retinal landmarks, particularly the fovea is crucial for diagnosing diab...

Two-stage color fundus image registration via Keypoint Refinement and Confidence-Guided Estimation.

Color fundus images are widely used for diagnosing diseases such as Glaucoma, Cataracts, and Diabeti...

Coptidis rhizoma and berberine as anti-cancer drugs: A 10-year updates and future perspectives.

Cancer continues to be among the most substantial health challenges globally. Among various natural ...

Machine Learning Models for Predicting Type 2 Diabetes Complications in Malaysia.

This study aimed to develop machine learning (ML) models to predict diabetic complications in patien...

Unsupervised machine learning analysis of optical coherence tomography radiomics features for predicting treatment outcomes in diabetic macular edema.

This study aimed to identify distinct clusters of diabetic macular edema (DME) patients with differe...

Impact of Simplera Sync™ sensors and Extended™ Wear Infusion Sets on glycaemia and system performance of the MiniMed™ 780G system in children and young adults with previously high HbA1c.

AIMS: The MiniMed™ 780G improves glycaemia and reduces burden in type 1 diabetes. We investigated ho...

Artificial intelligence to guide rituximab therapy in patients with phospholipase A2 receptor-associated membranous nephropathy.

Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined with routine blood tests.

Ischemic cerebral infarction is the most prevalent type of stroke, causing significant disability an...

Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0-3 and the development of a machine learning prediction model: a nationwide prospective cohort study.

BACKGROUND: The American Heart Association recently introduced the concept of cardiovascular-kidney-...

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals.

Diabetes is a growing global health concern, affecting millions and leading to severe complications ...