The Evidence for Automated Grading in Diabetic Retinopathy Screening

Alan      D. Fleming; Sam      Philip; Keith      A. Goatman; Gordon      J. Prescott; Peter      F. Sharp; John      A. Olson

doi:10.2174/157339911796397802

Abstract

Systematic screening for diabetic retinopathy using retinal photography has been shown to reduce the incidence of blindness among people with diabetes. The implementation of diabetic retinopathy screening programmes faces several challenges. Consequently, methods for improving the efficiency of screening are being sought, one of which is the automation of image grading involving detection of images with either disease or of inadequate quality using computer software. This review aims to bring together the available evidence that is suitable for making a judgement about whether automated grading systems could be used effectively in diabetic retinopathy screening. To do this, it focuses on studies made by the few centres who have presented results of tests of automated grading software on large sets of patients or screening episodes. It also considers economic model analyses and papers describing the effectiveness of manual grading in order that the effect of replacing stages of manual grading by automated grading can be judged. In conclusion, the review shows that there is sufficient evidence to suggest that automated grading, operating as a disease / no disease grader, is safe and could reduce the workload of manual grading in diabetic retinopathy screening.

Keywords: Diabetic retinopathy, Screening, Computer-assisted image analysis, Imaging, Telemedicine, Automated grading, Blindness, Slit-lamp examination, Microaneurysm detection, Haemorrhage