Background and objective: Diabetic retinopathy (DR) is a major complication of diabetes and the leading cause of blindness among US working-age adults. Detection of subclinical DR is important for disease monitoring and prevention of damage to the retina before occurrence of vision loss. The purpose of this retrospective study is to describe an automated method for discrimination of subclinical DR using fine structure analysis of retinal images.
Methods: Discrimination between nondiabetic control (NC; N = 16) and diabetic without clinical retinopathy (NDR; N = 17) subjects was performed using ordinary least squares regression and Fisher's linear discriminant analysis. A human observer also performed the discrimination by visual inspection of the images.
Results: The discrimination rate for subclinical DR was 88% using the automated method and higher than the rate obtained by a human observer which was 45%.
Conclusions: The method provides sensitive and rapid analysis of retinal images and could be useful in detecting subclinical DR.