Abstract
Diabetic retinopathy is one of the common complications of diabetes. Unfortunately, in many cases the patient is not aware of any symptoms until it is too late for effective treatment. Through analysis of evoked potential response of the retina, the optical nerve, and the optical brain center, a way will be paved for early diagnosis of diabetic retinopathy and prognosis during the treatment process. In this paper, we present an artificial-neural-network-based method to classify diabetic retinopathy subjects according to changes in visual evoked potential spectral components and an anatomically realistic computer model of the human eye under normal and retinopathy conditions in a virtual environment using 3D Max Studio and Windows Movie Maker.