Diabetes

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 screenings to prevent its progression to severe stages. Manual diagnosis is labor-intensive and prone to inaccuracies, highlighting the need for automated, accurate detection methods. This study proposes a novel approach for early DR detection by integrating advanced machine learning techniques. The p...

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 molecular alterations in metabolic dysfunction-associated steatotic liver disease (MASLD). We aimed to comprehensively characterize hepatic fiber morphological phenotypes in MASLD and their associated molecular alterations using multilayer omics analyses. APPROACH AND RESULTS: To quantify the morphological...

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 contributor to diabetic retinopathy (DR) progression. H...

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, deformities, and ulcers. Precise classification of diabetic...

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 diagnosis. Recognizing DR utilizing color fundus imagi...

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 and has become the most important cause of end-sta...

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

BACKGROUND: Diabetes mellitus, characterized by chronic glucose dysregulation, significantly increases the risk of neurological disorders such as cogn...

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 diabetic retinopathy and other ocular diseases. But ac...

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 Diabetic Retinopathy. The registration of color fundus im...

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 compounds, berberine, an isoquinoline alkaloid obt...

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 patients with type 2 diabetes (T2D) in Malaysia. Data fr...

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 differential anti-vascular endothelial growth factor (VEG...

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 how new all-in-one "Simplera Sync™" sensors and 7-da...

Assessing the association of multi-environmental chemical exposures on metabolic syndrome: A machine learning approach.

Metabolic syndrome (MetS) is a major global public health concern due to its rising prevalence and association with increased risks of cardiovascular ...

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 and death worldwide. Transient ischemic attack (TIA)...

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-metabolic (CKM) syndrome, highlighting the increas...

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 if not properly managed. The primary challenge in ...

Machine learning for high-risk hospitalization prediction in outpatient individuals with diabetes at a tertiary hospital.

OBJECTIVE: To characterize, via a predictive model using real-world data, patients with diabetes with a heightened probability of hospitalization.

Detection of diabetic macular oedema patterns with fine-grained image categorisation on optical coherence tomography.

PURPOSE: To develop an artificial intelligence (AI) system for detecting pathological patterns of diabetic macular oedema (DME) with fine-grained imag...