Diabetes

Explainable AI for diabetic retinopathy detection using vision transformers.

The heterogeneous acquisition, variability of orientation, and subtle lesions continue to challenge the screening of diabetic retinopathy through color fundus photographs. We formulate DR grading as a binary triage task (No-DR vs DR) and propose the All-ViT Hybrid framework, integrating complementary pretrained transformer backbones within a stability-oriented training schedule (head-only warm-up,...

Machine learning versus deep learning for screening ischemic stroke among asymptomatic population.

Ischemic stroke puts great health burden in public. However, the diagnosis is based on head CT or MRI scanning. We aim to develop classifier models to screen asymptomatic ischemic stroke based on clinical data without imaging. The subjects (age ≥ 20 years) were recruited into our study who attended health examination. We developed models to classify subjects with or without ischemic stroke using s...

Non-invasive imaging diagnostic techniques in metabolic dysfunction-associated steatotic liver disease: a roadmap for clinicians.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed nonalcoholic fatty liver disease (NAFLD), is the most prevalent ch...

An ensemble of deep learning models with falcon optimization assisted diabetic retinopathy diagnosis on retinal fundus images.

Diabetic Retinopathy (DR) is a prominent results of diabetes mellitus that causes abnormalities lesions in retina. If not identified at early, it may ...

The diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care: a prospective validation study.

OBJECTIVES: This study investigated the diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care, using ophthalmologist-led s...

Engagement with an AI- and CGM-Integrated Digital Health Platform Is Associated with Clinically Significant Weight Loss.

BACKGROUND: Obesity affects millions of U.S. adults and is linked to cardiometabolic disease. This study examined whether user engagement with the Sig...

Advances in diabetes technology: clinical implications, limitations, and future perspectives.

Diabetes technology has transformed substantially over the past two decades, becoming central to modern diabetes management. Continuous glucose monito...

Artificial Intelligence in Self-Management of Gestational Diabetes Mellitus: A Systematic Review.

The prevalence of gestational diabetes mellitus (GDM) continues to rise, necessitating reliable and effective self-management strategies to improve ma...

Multifunctional Bioengineered Scaffolds and Adjunct Therapeutic Strategies for Diabetic Foot Ulcers.

Diabetic foot ulcers (DFUs) are chronic, non-healing wounds that affect up to 34% of diabetic patients. DFUs are complicated by infection in nearly 60...

Comparison of ChatGPT and dietitians in formulating diet plans and recommendations for patients with cardiometabolic diseases.

BACKGROUND: Artificial Intelligence (AI) is transforming personalized medicine, yet its efficacy constitutes a dynamic factor in the field of health a...

DeepAdapter: a generalisable algorithm integrating self-supervised learning and unsupervised domain adaptation for robust retinopathy of prematurity screening.

AIMS: To develop and validate DeepAdapter, a novel deep learning algorithm that integrates self-supervised learning (SSL) and unsupervised domain adap...

Spinal cord FDG PET uptake in healthy adults: an AI-driven segmentation study.

PURPOSE: 18F-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET)/Computerized Tomography (CT) is an important imaging modality in oncology, bu...

Swin-DRNet: A robust transformer framework for diabetic retinopathy screening under heterogeneous imaging conditions.

Diabetic retinopathy (DR) has been known as one of the leading preventable causes of vision impairment globally and requires automated screening syste...

Development and prospective evaluation of a real-time deep learning model for inpatient hypoglycemia prediction.

Inpatient hypoglycemia is associated with increased morbidity, mortality, length of stay, and healthcare costs, yet current management remains reactiv...

A machine learning model integrating two-dimensional ultrasound radiomics and clinical parameters for differentiating membranous nephropathy from IgA nephropathy.

BACKGROUND: Membranous nephropathy (MN) and IgA nephropathy (IgAN) are the two most common primary glomerular diseases in China, with distinct pathoph...

Machine learning-based prediction of mortality and hospitalization in diabetic patients with heart failure with preserved ejection fraction: the GUARDIAN-P risk score.

AIMS: Diabetes mellitus (DM) is a major contributor to adverse outcomes in patients with heart failure with preserved ejection fraction (HFpEF). We ai...

Integrative transcriptomic and machine learning analysis identifies key extracellular matrix-related genes in diabetic retinopathy.

Extracellular matrix (ECM) remodeling contributes to retinal vascular basement membrane thickening, an early structural hallmark of diabetic retinopat...

Head-to-Head Comparative Evaluation of Four Commercially Available Artificial Intelligence Systems for Detecting Referable Diabetic Retinopathy in a Tanzanian Population.

OBJECTIVE: Comparative evaluations of commercially available artificial intelligence (AI) systems for use in diabetic retinopathy (DR) screening, part...

Identifying PLAU as a shared pathogenic gene in type 2 diabetes and bladder urothelial carcinoma through integrated transcriptomic analysis and machine learning for diagnostic and therapeutic value.

Type 2 diabetes mellitus (T2DM) and bladder urothelial carcinoma (BLCA) are two kinds of diseases that seriously threaten human health. Their pathogen...

Biomarker-driven diagnostics and personalized treatment in polycystic ovary syndrome.

Polycystic Ovary Syndrome (PCOS) is a common and clinically heterogeneous endocrine disorder affecting approximately 4-20% of women worldwide. Its com...