Comparison of automated and expert human grading of diabetic retinopathy using smartphone-based retinal photography.

ABSTRACT:

Purpose

The aim of this study is to investigate the efficacy of a mobile platform that combines smartphone-based retinal imaging with automated grading for determining the presence of referral-warranted diabetic retinopathy (RWDR).

Methods

A smartphone-based camera (RetinaScope) was used by non-ophthalmic personnel to image the retina of patients with diabetes. Images were analyzed with the Eyenuk EyeArt® system, which generated referral recommendations based on presence of diabetic retinopathy (DR) and/or markers for clinically significant macular oedema. Images were independently evaluated by two masked readers and categorized as refer/no refer. The accuracies of the graders and automated interpretation were determined by comparing results to gold standard clinical diagnos

SUBMITTER: Kim TN 

PROVIDER: S-EPMC7852658 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature