Results of Automated Retinal Image Analysis for Detection of Diabetic Retinopathy from the Nakuru Study, Kenya.

Abstract

OBJECTIVE:Digital retinal imaging is an established method of screening for diabetic retinopathy (DR). It has been established that currently about 1% of the world's blind or visually impaired is due to DR. However, the increasing prevalence of diabetes mellitus and DR is creating an increased workload on those with expertise in grading retinal images. Safe and reliable automated analysis of retinal images may support screening services worldwide. This study aimed to compare the Iowa Detection Program (IDP) ability to detect diabetic eye diseases (DED) to human grading carried out at Moorfields Reading Centre on the population of Nakuru Study from Kenya. PARTICIPANTS:Retinal images were taken from participants of the Nakuru Eye Disease Study in Kenya in 2007/08 (n = 4,381 participants [NW6 Topcon Digital Retinal Camera]). METHODS:First, human grading was performed for the presence or absence of DR, and for those with DR this was sub-divided in to referable or non-referable DR. The automated IDP software was deployed to identify those with DR and also to categorize the severity of DR. MAIN OUTCOME MEASURES:The primary outcomes were sensitivity, specificity, and positive and negative predictive value of IDP versus the human grader as reference standard. RESULTS:Altogether 3,460 participants were included. 113 had DED, giving a prevalence of 3.3% (95% CI, 2.7-3.9%). Sensitivity of the IDP to detect DED as by the human grading was 91.0% (95% CI, 88.0-93.4%). The IDP ability to detect DED gave an AUC of 0.878 (95% CI 0.850-0.905). It showed a negative predictive value of 98%. The IDP missed no vision threatening retinopathy in any patients and none of the false negative cases met criteria for treatment. CONCLUSIONS:In this epidemiological sample, the IDP's grading was comparable to that of human graders'. It therefore might be feasible to consider inclusion into usual epidemiological grading.