AIM: To identify combinations of up to three visual function tests with the best performance for classifying diabetic retinopathy (DR) severity stage. To describe in detail the measurements from a comprehensive set of visual function tests. METHODS: ...
Diabetic Retinopathy, Cataract, and Glaucoma are major retinal diseases that require early detection to prevent irreversible vision loss. This study proposes a deep learning-based framework for the automated classification of retinal images into four...
The rising prevalence of retinal diseases is a significant concern, as certain untreated conditions can lead to severe vision impairment or even blindness. Deep learning algorithms have emerged as a powerful tool for the diagnosis and analysis of med...
BACKGROUND: Evidence regarding the association between physical activity (PA) and diabetic retinopathy (DR) remains inconsistent. Furthermore, its effects on retinal vessel diameters in type 2 diabetes are not well established. We aimed to investigat...
BACKGROUND: Diabetic retinopathy (DR) is the main cause of blindness worldwide, and its prevalence rate is constantly rising. More in-depth exploration of its risk factors and pathogenic mechanisms is needed.
OBJECTIVE: To evaluate the effectiveness of a deep learning-based style adaptation strategy in improving the diagnostic accuracy and cross-camera generalisability of artificial intelligence (AI) for detecting diabetic retinopathy (DR).
Diabetic retinopathy (DR) is a leading cause of preventable vision loss. While DR screening is critical, evidence on the reach and implementation of different screening models in primary healthcare settings is limited. This study evaluated the reach ...
To develop our proposed technology method to improve retinal pigment epithelium (RPE) detection in optical coherence tomography (OCT) images and compare its efficacy with Topcon's automated segmentation algorithm across multiple retinal diseases and ...
Diabetic Retinopathy (DR) remains a leading cause of vision loss globally, necessitating accurate and scalable diagnostic solutions. Existing Deep Learning (DL) models often underutilize lesion-specific cues that are critical for early DR grading, wh...
AI-based diabetic retinopathy (DR) screening algorithms have been evaluated in many countries and have shown promise in expanding access to screening, especially in low- and middle-income countries (LMICs). However, the literature lacks guidance on w...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.