Automated Detection and Classification of Telemedical Retinopathy of Prematurity Images

Background: Retinopathy of prematurity (ROP) is a retinal disorder of low birth weight infants and it is the leading cause of childhood blindness. The capability of wide field digital imaging systems to capture the clinical features of ROP has greatly helped the physicians to assess the severity of ROP and prevent childhood blindness due to ROP. Currently there is a lack of automated systems to assess the severity of ROP to assist the ROP specialist to make treatment decision. Objective: To present an automated detection and classification approach to assess the severity of ROP using wide field telemedical images. Materials and Methods: A total of 160 telemedical ROP (tele-ROP) images were collected out, of which 36 images were Normal, 79 images were Stage 2, and 45 images were Stage 3. Hessian analysis and support vector machine (SVM) classifier have been used to detect and classify the severity of ROP from tele-ROP images. Results: Classified the Normal, Stage 2, and Stage 3 images using SVM. Achieved accuracy of 91.8%, sensitivity of 90.37%, specificity of 94.65%, false positive rate of 5.35%, and false negative rate of 9.63%. Conclusions: The automated approach of detecting and classifying ROP would support pediatric ophthalmologists for early treatment decisions with optimal care.