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

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

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

Aims/introduction: Diabetic retinopathy (DR) often remains asymptomatic until it reaches advanced stages, when delayed treatment can lead to irreversible visual impairment. To promote timely ...

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

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 ...

A deep learning system can accurately detect vision-threatening diabetic retinopathy. A dual-modality, deep learning system can accurately detect vision-threatening diabetic retinopathy (vtDR) using ...

Artificial intelligence is transforming the detection of diabetic retinopathy, moving away from traditional machine learning models that rely on manually created features to deep learning methods that ...

Diabetic retinopathy is one of the main causes of blindness in diabetics and early detection and classification are essential for both prevention and successful treatment. The subjective and ...