Automated Telemedicine Image Analysis for Diabetic Retinopathy Screening

January 2019
Connecticut Medicine;Jan2019, Vol. 83 Issue 1, p21
Academic Journal
Objective: To evaluate the efficacy of an automated retinal lesion detection system for screening of diabetic retinopathy (DR) in a large atrisk population. Methods: This retrospective study compared interpretation of nonmydriatic fundus photos taken in a primary care clinic by an automated image analysis program created by Siemens Corporate Research to that of a retina specialist. Results: A total of 1906 fundus photographs were analyzed from 925 patients. An overall sensitivity of 78.3% and specificity of 43.8% was obtained for all of the images analyzed for any stage of DR. Sensitivities for detection of referable DR were 67.7% - 93.8% for images of adequate quality and 87.9% - 100% for images of good quality. Conclusions: This study shows high sensitivity and moderate specificity for automated detection of referable DR in an at-risk primary-care population. As technology improves, automated image interpretation may be used to provide screening to individuals who may otherwise not have access to needed ophthalmic care.


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