The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy. Issue 6 (5th August 2009)

• Record Type: Journal Article
• Authors: Fleming, Alan D; Goatman, Keith A; Philip, Sam; Williams, Graeme J; Prescott, Gordon J; Scotland, Graham S; McNamee, Paul; Leese, Graham P; Wykes, William N; Sharp, Peter F; Olson, John A
• Is Part Of: British journal of ophthalmology. Volume 94:Issue 6(2010)
• Page Start: 706
• Page End: 711
• Publication Date: 2009-08-05
• Subjects: Pattern recognition -- automated -- vision screening -- diabetic retinopathy -- retina -- photography -- imaging; Ophthalmology -- Periodicals; 617.7
• DOI: 10.1136/bjo.2008.149807
• Languages: English
• ISSNs: 0007-1161

Abstract

Abstract : Background/aims: Automated grading has the potential to improve the efficiency of diabetic retinopathy screening services. While disease/no disease grading can be performed using only microaneurysm detection and image-quality assessment, automated recognition of other types of lesions may be advantageous. This study investigated whether inclusion of automated recognition of exudates and haemorrhages improves the detection of observable/referable diabetic retinopathy. Methods: Images from 1253 patients with observable/referable retinopathy and 6333 patients with non-referable retinopathy were obtained from three grading centres. All images were reference-graded, and automated disease/no disease assessments were made based on microaneurysm detection and combined microaneurysm, exudate and haemorrhage detection. Results: Introduction of algorithms for exudates and haemorrhages resulted in a statistically significant increase in the sensitivity for detection of observable/referable retinopathy from 94.9% (95% CI 93.5 to 96.0) to 96.6% (95.4 to 97.4) without affecting manual grading workload. Conclusion: Automated detection of exudates and haemorrhages improved the detection of observable/referable retinopathy.