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.