Automated grading for diabetic retinopathy: a large-scale audit using arbitration by clinical experts. Issue 12 (21st September 2010)

• Record Type: Journal Article
• Authors: Fleming, Alan D; Goatman, Keith A; Philip, Sam; Prescott, Gordon J; Sharp, Peter F; Olson, John A
• Is Part Of: British journal of ophthalmology. Volume 94:Issue 12(2010)
• Page Start: 1606
• Page End: 1610
• Publication Date: 2010-09-21
• Subjects: Computer-assisted image analysis -- diabetic retinopathy -- imaging -- macula -- public health -- retina -- screening -- telemedicine; Ophthalmology -- Periodicals; 617.7
• DOI: 10.1136/bjo.2009.176784
• Languages: English
• ISSNs: 0007-1161

Abstract

Abstract : Background/aims: Automated grading software has the potential to reduce the manual grading workload within diabetic retinopathy screening programmes. This audit was undertaken at the request of Scotland's National Diabetic Retinopathy Screening Collaborative to assess whether the introduction of automated grading software into the national screening programme would be safe, robust and effective. Methods: Automated grading, performed by software for image quality assessment and for microaneurysm/dot haemorrhage detection, was carried out on 78 601 images, obtained from 33 535 consecutive patients, which had been manually graded at one of two regional diabetic retinopathy screening programmes. Cases where the automated grading software assessment indicated gradable images with no disease but the screening programme indicated ungradable images or disease more severe than mild retinopathy were arbitrated by seven senior ophthalmologists. Results: 100% (180/180) of patients with proliferative retinopathy, 100% (324/324) with referable background retinopathy, 100% (193/193) with observable background retinopathy, 97.3% (1099/1130) with referable maculopathy, 99.2% (384/387) with observable maculopathy and 99.8% (1824/1827) with ungradable images were detected by the software. Conclusion: The automated grading software operated to previously published results when applied to a large, unselected population attending two regional screening programmes. Manual grading workload reduction would be 36.3%.