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Nicolai Larsen, Jannik Godt, Michael Grunkin, Henrik Lund-Andersen, Michael Larsen; Automated Detection of Diabetic Retinopathy in a Fundus Photographic Screening Population. Invest. Ophthalmol. Vis. Sci. 2003;44(2):767-771. doi: 10.1167/iovs.02-0417.
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purpose. To evaluate the performance of an automated fundus photographic image-analysis algorithm in high-sensitivity and/or high-specificity segregation of patients with diabetes with untreated diabetic retinopathy from those without retinopathy.
methods. This was a retrospective cross-sectional study of 260 consecutive nonphotocoagulated eyes in 137 diabetic patients attending routine photographic retinopathy screening. Mydriatic 60° fundus photography on 35-mm color transparency film was used, with a single fovea-centered field. Routine grading was based on visual examination of slide-mounted transparencies. Reference grading was performed with specific emphasis on achieving high sensitivity. Computer-assisted automated red lesion detection was performed on digitized transparencies.
results. When applied in a screening population comprising patients with diabetes with untreated diabetic retinopathy in any eye and patients with diabetes without retinopathy, the automated lesion detection correctly identified 90.1% of patients with retinopathy and 81.3% of patients without retinopathy. A per-eye analysis for methodological purposes demonstrated that the automated lesion detection could be adapted to simulate various visual evaluation strategies. When adapted at high sensitivity, the automated system demonstrated sensitivity at 93.1% and specificity at 71.6%. When adapted at high specificity the automated system demonstrated sensitivity at 76.4% and specificity at 96.6%, closely matching routine visual grading at sensitivity 76.4% and specificity 98.3%.
conclusions. Automated detection of untreated diabetic retinopathy in fundus photographs from a screening population of patients with diabetes can be made with adjustable priority settings, emphasizing high-sensitivity identification of diabetic retinopathy or high-specificity identification of absence of retinopathy, covering opposing extremes of visual evaluation strategies demonstrated by human observers.
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