Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening.

AIMS: To develop a system to detect automatically features of diabetic retinopathy in colour digital retinal images and to evaluate its potential in diabetic retinopathy screening. METHODS: Macular centred 45 degrees colour retinal images from 1273 patients in an inner city diabetic retinopathy screening programme. A system was used involving pre-processing to standardize colour and enhance contrast, segmentation to reveal possible lesions and classification of lesions using an artificial neural network. The system was trained using a subset of images from 500 patients and evaluated by comparing its performance with a human grader on a test set of images from 773 patients. RESULTS: Maximum sensitivity for detection of any retinopathy on a per patient basis was 95.1%, accompanied by specificity of 46.3%. Specificity could be increased as far as 78.9% but was accompanied by a fall in sensitivity to 70.8%. At a setting with 94.8% sensitivity and 52.8% specificity, no cases of sight-threatening retinopathy were missed (retinopathy warranting immediate ophthalmology referral or re-examination sooner than 1 year by National Institute for Clinical Excellence criteria). If the system was implemented at 94.8% sensitivity setting over half the images with no retinopathy would be correctly identified, reducing the need for a human grader to examine images in 1/3 of patients. CONCLUSION: This system could be used when screening for diabetic retinopathy. At 94.8% sensitivity setting the number of normal images requiring examination by a human grader could be halved.