Assessment of Automated Screening for Treatment-Requiring Diabetic Retinopathy

Abstract

Purpose: To evaluate fundus photographic image analysis combining automated detection of red lesions, bright lesions, and image quality as a means of identifying treatment-requiring diabetic retinopathy in a screening population of diabetic patients. Methods: This was a retrospective cross-sectional study of 106 patients from a diabetic retinopathy screening clinic referred for photocoagulation treatment in the period from January 1996 to May 2002 on the basis of mydriatic 60-degree 35-mm color transparency fundus photography. One fovea-centered fundus photograph and one centered nasal of the optic disk from each of a subject's two eyes was selected for digitization and analyzed using a previously tested computerized red-lesion detection algorithm in combination with a new algorithm for detection of bright lesions and image quality. The algorithm was calibrated on an independent set of fundus photographs. Results: Automated red-lesion detection identified 104 of 106 patients requiring photocoagulation treatment, whereas bright-lesion detection identified only 91 of the 106 patients. Two patients who were not identified by either lesion detection algorithm were automatically detected as having poor image quality in one or both eyes. In the study sample, the risk of missing treatment-requiring retinopathy patients from being detected was 0.0% (estimated CI95 0.0–3.4%). Conclusions: The combination of automated detection of red lesions and poor image quality identified all treatment-requiring diabetic retinopathy patients in the study sample. No additional information was contributed by the automated bright-lesion detection.