Implementation and Evaluation of a Mobile Retinal Image Acquisition System for Screening Diabetic Retinopathy: Study Protocol

Abstract

Screening diabetic retinopathy, a major cause of blindness, is time-consuming for ophthalmologists and has some constrains in achieving full coverage and attendance. The handheld fundus camera EyeFundusScope was recently developed to expand the scale of screening, drawing on images acquired in primary care and telescreening made by ophthalmologists or a computer-aided diagnosis (CADx) system. This study aims to assess the diagnostic accuracy of the interpretation of images captured using EyeFundusScope and perform its technical evaluation, including image quality, functionality, usability, and acceptance in a real-world clinical setting. Physicians and nurses without training in ophthalmology will use EyeFundusScope to take pictures of the retinas of patients with diabetes and the images will be classified for the presence or absence of diabetic retinopathy and image quality by a panel of ophthalmologists. A subgroup of patients will also be examined with the reference standard tabletop fundus camera. Screening results provided by the CADx system on images taken with EyeFundusScope will be compared against the ophthalmologists’ analysis of images taken with the tabletop fundus camera. Diagnostic accuracy measures with 95% confidence intervals (CIs) will be calculated for positive and negative test results. Proportion of each category of image quality will be presented. Usability and acceptance results will be presented qualitatively.

Diabetic Retinopathy Grading by Deep Graph Correlation Network on Retinal Images Without Manual Annotations

“…Diabetic retinopathy is a severe etiology of vision loss, possibly causing blindness, which can be avoided by early diagnosis, fundus screening, and temporal treatment ( 4 ). Fundus images with professional interpretation are a commonly accepted screening strategy for blindness prevention, with superior capability for in-person dilated eye examinations ( 5 ). However, the procedures for observing diabetic retinopathy confront challenges by factors correlated to execution, availability of professionals, and substantial financial sustainability ( 6 – 9 ).…”

Early Detection of Diabetic Retinopathy Based Artificial Intelligent Techniques

“…The history of the patient and the results of the physical examination are the two primary sources of diagnostic information. [1][2]. DR is a complication of diabetes that affects the eyes.…”