Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy

Abstract

Aims: This study aimed to evaluate automated fundus photograph analysis algorithms for the detection of primary lesions and a computer-assisted diagnostic system for grading diabetic retinopathy (DR) and the risk of macular edema (ME).

Methods: Two prospective analyses were conducted on fundus images from diabetic patients. Automated detection of microaneurysms and exudates was applied to two small image databases on which these lesions were manually marked. A computer-assisted diagnostic system for the detection and grading of DR and the risk of ME was then developed and evaluated, using a large database containing both normal and pathological images, and compared with manual grading.

Results: The algorithm for the automated detection of microaneurysms demonstrated a sensitivity of 88.5%, with an average number of 2.13 false positives per image. The pixel-based evaluation of the algorithm for automated detection of exudates had a sensitivity of 92.8% and a positive predictive value of 92.4%. Combined automated grading of DR and risk of ME was performed on 761 images from a large database. For DR detection, the sensitivity and specificity of the algorithm were 83.9% and 72.7%, respectively, and, for detection of the risk of ME, the sensitivity and specificity were 72.8% and 70.8%, respectively.

Conclusion: This study shows that previously published algorithms for computer-aided diagnosis is a reliable alternative to time-consuming manual analysis of fundus photographs when screening for DR. The use of this system would allow considerable timesavings for physicians and, therefore, alleviate the time spent on a mass-screening programme.