Artificial intelligence has rapidly evolved from the experimental phase to the implementation
phase in many image-driven clinical disciplines, including ophthalmology. A combination of …

Deep learning (DL) has revolutionized the field of computer vision and image processing. In
medical imaging, algorithmic solutions based on DL have been shown to achieve high …

Background Visual impairment from diabetic retinopathy (DR) is an increasing global public
health concern, which is preventable with screening and early treatment. Digital retinal …

This document represents a brief account of ongoing project for Diabetic Retinopathy
Detection (DRD) through integration of state-of the art Deep Learning methods. We make …

Background The aim of the project was to develop a novel method for diabetic retinopathy
screening based on the examination of tear fluid biomarker changes. In order to evaluate the …

Introduction: Urbanization has caused dramatic changes in lifestyle, and these rapid
transitions have led to an increased risk of noncommunicable diseases, such as type 2 …

Background/aims To determine the diagnostic accuracy of trained rural ophthalmologists
and non-medical image graders in the assessment of diabetic retinopathy (DR) in rural …

Background. Up to 98% of visual loss secondary to diabetic retinopathy (DR) can be
prevented with early detection and treatment. Despite this, less than 50% of Australian and …

Objective: To determine the diagnostic accuracy of non-mydriatic fundus camera for the
detection of diabetic retinopathy. Methods: The cross-sectional study was conducted at Al …

Background: Visual impairment and blindness from diabetic retinopathy (DR), which can be
reduced by early screening and treatment, is an emerging public health concern in low …