Diabetic retinopathy (DR) is a frequent microvascular complication of diabetes and a leading cause of blindness worldwide. However, much of this diabetic blindness can be delayed or even prevented with timely diagnosis and proper treatment. For this reason, regular screening and early detection of potentially sight-threatening retinopathy is a key strategy. Currently in the UK trained graders are used for screening. This study looks at the sensitivity and specificity of an automated algorithm for detecting referral-warranted DR on Optos ultrawidefield (UWF) pseudocolour images. A total of 383 subjects (754 eyes) were enrolled. Nonproliferative DR graded to be moderate or higher on the 5-level International Clinical Diabetic Retinopathy (ICDR) severity scale was considered as grounds for referral. The software automatically detected DR lesions using the previously trained classifiers and classified each image in the test set as referral-warranted or not warranted. Sensitivity, specificity and the area under the receiver operating curve (AUROC) of the algorithm were computed. The automated algorithm achieved a 91.7%/90.3% sensitivity (95% CI 90.1–93.9/80.4–89.4) with a 50.0%/53.6% specificity (95% CI 31.7–72.8/36.5–71.4) for detecting referral-warranted retinopathy at the patient / eye levels, respectively; the AUROC was 0.873/0.851 (95% CI 0.819–0.922/0.804–0.894). They concluded that DR lesions were detected from Optos pseudocolour UWF images using an automated algorithm. Images were classified as referral-warranted DR with a high degree of sensitivity and moderate specificity. Automated analysis of UWF images could be of value in DR screening programmes and could allow for more complete and accurate disease staging.
Automated grading of diabetic retinopathy: is it possible?
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