The advent of deep learning in ophthalmology has revolutionised the detection and diagnosis of diabetic retinopathy (DR). By utilising convolutional neural networks (CNNs) and advanced image ...

Diabetic patients with high myopia have a significantly lower choroidal vascularity index (CVI) than those with diabetic retinopathy (DR), according to research published in Frontiers in Cell and ...

Introduction to Computer Vision in HealthcareLet’s face it—healthcare can be slow, expensive, and prone to human error. But what if a camera and some smart algorithms could help change that? That's ...

AI offers promise in the realm of chronic disease management, including diabetes, obesity, and PCOS, but must be approached with caution.

An artificial intelligence (AI) model integrated into a retina tracker for diabetic retinopathy can achieve high accuracy ...

A novel AI-powered retina tracker can analyze retinal images with near-perfect accuracy in under one second, according to a ...

A deep learning system can accurately detect vision-threatening diabetic retinopathy, demonstrating specialist-level diagnostic performance.

Artificial Intelligence (AI) algorithms are revolutionizing the way retina images are analyzed for detecting conditions such as age-related macular degeneration (AMD) and diabetic retinopathy. These ...

Diabetic retinopathy (DR) remains the leading cause of vision loss and blindness in people of working age, in spite of the fact that current treatments are effective.

Microaneurysm (MA) turnover increases with the progression of nonproliferative diabetic retinopathy (NPDR) and is correlated with presence of intraretinal microvascular abnormalities (IRMAs), ...