Diabetes

Contextual anatomy-guided deep learning for accurate fovea segmentation in diabetic retinopathy fundus images.

Accurate fovea segmentation in fundus images is a critical step in diabetic retinopathy screening; however, it remains a challenging task due to the indistinct boundaries of the fovea. Beyond simple localization, precise segmentation offers essential clinical value for Diabetic Macular Edema (DME) management, as treatment decisions-specifically the choice between intravitreal anti-VEGF injection f...

Machine learning prediction of metabolic-associated fatty liver disease in type 2 diabetes: Emphasizing data imputation and feature selection.

Metabolic-Associated Fatty Liver Disease (MAFLD) is common among Type 2 Diabetes (T2DM) patients. The coexistence of these conditions increases the risk of MAFLD progression and diabetes complications. Detecting MAFLD early is challenging due to its asymptomatic initial stages. In this study, we aimed to develop a machine learning model to predict MAFLD in T2DM patients. We conducted a cross-secti...

Environmental toxicant exposure and diabetes risk: An exposome-wide association study integrating mixture effects and molecular mechanisms.

Environmental toxicant exposure has emerged as a potential contributor to diabetes, yet systematic investigations integrating multiple chemicals and b...

Machine Learning Integration of MRI Intratumoral and Peritumoral Radiomics Features for Predicting PNSTs Postoperative Complications.

RATIONALE AND OBJECTIVES: Postoperative complications (15-76%) substantially affect patients with peripheral nerve sheath tumors (PNSTs), yet objectiv...

Cardiorespiratory Markers of Type 2 Diabetes: Machine Learning-Based Analysis.

BACKGROUND: The global prevalence of type 2 diabetes mellitus (T2DM) poses significant challenges due to its association with increased cardiovascular...

ChatGPT Health performance in a structured test of triage recommendations.

ChatGPT Health launched in January 2026 as OpenAI's consumer health tool, reaching millions of users. Here, we conducted a structured stress test of t...

The power of machine learning models in predicting gestational diabetes mellitus.

Subtypes of newly diagnosed type 2 diabetes and risk of complications: analysis of electronic health records in the USA.

AIMS/HYPOTHESIS: Data-driven subtyping of type 2 diabetes has not been translated into clinical practice due to the lack of routine fasting glucose an...

Machine learning-based prediction of persistent diabetic macular edema using OCT-derived structural biomarkers and clinical features.

BACKGROUND: Persistent diabetic macular edema (DME) remains a leading cause of vision loss in diabetic retinopathy, even with anti-VEGF therapy. About...

The shared mechanisms and potential diagnostic markers for IgA nephropathy and celiac disease.

IgA nephropathy (IgAN) and celiac disease (CeD) are autoimmune disorders characterized by dysregulated immune responses; however, the molecular mechan...

Smart Kiosk for Nutritional Management of People With Diabetes in Underserved Communities: Development and Technical Evaluation.

BACKGROUND: Diabetes is a chronic disease with a high global prevalence, increasing from 200 million people in 1990 to 830 million in 2022, with a hig...

Automated Report Generation in Ophthalmology: Integrating Artificial Intelligence, Multimodal Imaging, and Clinical Data.

Artificial intelligence (AI) has emerged as a transformative force in ophthalmology, enabling automated, accurate, and efficient clinical reporting. T...

OPU-NET-DADENA: Optimized deep learning ensemble with u-net segmentation for early detection of diabetic retinopathy.

One of the most significant microvascular complications of diabetes mellitus (DM) is diabetic retinopathy (DR). In the early stages, patients with DR ...

Novel Systemic Associations of Idiopathic Epiretinal Membrane Identified via Machine Learning.

PURPOSE: To discover novel systemic associations that may lead to idiopathic epiretinal membrane (iERM) using interpretable machine learning models. D...

Hepatic and abdominal adiposity in type 2 diabetes as assessed with machine learning on computed tomography scans.

AIMS: The combined assessment of multiple abdominal imaging traits in relation to type 2 diabetes remains incompletely characterised. The study examin...

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

BACKGROUND: Diabetes distress is common in patients with type 1 diabetes mellitus (T1DM). The aim of this study was to construct and validate predicti...

Bioactive Substances Characterization in Probiotic-Fermented Guava (Psidium guajava L.) Juice and Systematic Evaluation of its Anti-Hyperglycemic Activity.

The development of natural anti-hyperglycemic agents with minimal side effects is a critical objective in functional food research. This study compreh...

Stacked ensemble learning and in-silico profiling reveal dual DPP-IV and SGLT2 inhibitors from Moringa oleifera metabolites.

Diabetes mellitus (DM) is a growing global health challenge, particularly in low-resource settings where access to effective therapies remains limited...

Identifying a fatty acid metabolic gene signature in diabetic cardiomyopathy through integrated bioinformatics and machine learning.

BACKGROUND: Diabetic cardiomyopathy (DCM) is a major cardiovascular complication of diabetes mellitus, ultimately progressing to heart failure and inc...

Hypoglycemia problem-solving ability as a key predictor of severe hypoglycemia: a machine learning approach in adults with type 1 diabetes.

AIMS/INTRODUCTION: Severe hypoglycemia (SH) is a major complication in adults with type 1 diabetes mellitus (T1DM). The multifactorial etiology of T1D...