Improved automated screening of diabetic retinopathy.

AIM: To assess a two-step automated system (RetmarkerSR) that analyzes retinal photographs to detect diabetic retinopathy for the purpose of reducing the burden of manual grading.

METHODS: Anonymous images from 5,386 patients screened in 2007 were obtained from a nonmydriatic diabetic retinopathy screening program in Portugal and graded by an experienced ophthalmologist. RetmarkerSR earmarked microaneurysms, generating two outputs: 'disease' or 'no disease'. A second-step analysis, based on coregistration, combining two visits, was subsequently performed in 289 patients who underwent repeated examinations in 2008. The study was extended by analyzing all referrals considered urgent by the ophthalmologist from 2001 to 2007. Results were compared with those obtained by manual grading.

RESULTS: The RetmarkerSR classified in a first-step analysis 2,560 patients (47.5%) as having 'no disease' and 2,826 patients (52.5%) as having 'disease', thus requiring manual grading. RetmarkerSR detected all eyes considered urgent referrals. The two-step analysis further reduced the number of false-positive results by 26.3%, indicating an overall sensitivity of 95.8% and a specificity of 63.2%.

CONCLUSION: Automated grading of diabetic retinopathy may safely reduce the burden of grading patients in diabetic retinopathy screening programs. The novel two-step automated analysis system offers improved sensitivity and specificity over published automated analysis systems.