Automated detection of diabetic retinopathy lesions on ultrawidefield pseudocolour images. Issue 2 (19th September 2017)

• Record Type: Journal Article
• Authors: Wang, Kang; Jayadev, Chaitra; Nittala, Muneeswar G.; Velaga, Swetha B.; Ramachandra, Chaithanya A.; Bhaskaranand, Malavika; Bhat, Sandeep; Solanki, Kaushal; Sadda, SriniVas R.
• Is Part Of: Acta ophthalmologica. Volume 96:Issue 2(2018)
• Page Start: e168
• Page End: e173
• Publication Date: 2017-09-19
• Subjects: automated -- diabetic retinopathy -- pseudocolour -- ultrawidefield; Ophthalmology -- Periodicals; 617.7005
• DOI: 10.1111/aos.13528
• Languages: English
• ISSNs: 1755-375X

Abstract

Abstract: Purpose: We examined the sensitivity and specificity of an automated algorithm for detecting referral‐warranted diabetic retinopathy (DR) on Optos ultrawidefield (UWF) pseudocolour images. Methods: Patients with diabetes were recruited for UWF imaging. 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. Results: 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). Conclusion: Diabetic retinopathy (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.