Web-based screening for diabetic retinopathy in a primary care population: The EyeCheck Project

The objective of this study was to evaluate the feasibility of ATA category 2 Web-based screening for diabetic retinopathy in a primary care population in the Netherlands. A total of 1,676 patients in a primary care setting, with diabetes, without known diabetic retinopathy, and without previous screening by an ophthalmologist, were included between January 1 and December 31, 2003. Patients underwent a brief questionnaire and two field retinal photography. Photographs were independently read by two ophthalmologists. Outcome measures were gradability of the photographs, need for pharmacologic pupil dilation, assessment as suspect for presence of diabetic retinopathy, of neovascularization and of diabetic retinopathy, and agreement between graders. Of the population studied, 11.3% of patients required pupil dilation, average transmission time of images was 73 sec, 12.0% of patients had ungradable photographs, 10.4% of the patients with gradable photographs were assessed as "suspect for diabetic retinopathy," and 2.0% were assessed to need urgent referral. Red lesions were present in 3.5% and bright lesions were present in 1.6% of all gradable patients. Neovascularization of the disk was found in one patient. Type 1 patients had much higher rates of "suspect for diabetic retinopathy" (34.5%) than type 2 patients (9.4%). Interrater agreement κ was 0.93. Web-based screening, using open source technology and uncompressed images, is feasible in a primary care setting, with a high rate of inter-rater agreement. Dilate-as-needed may be a sensible approach for retinal photography. The high incidence of "suspect for diabetic retinopathy" in type 1 diabetes patients illustrates that web-based diabetic retinopathy screening programs for these patients may detect diabetic retinopathy that would otherwise have gone undetected

Automated Detection and Differentiation of Drusen, Exudates, and Cotton-Wool Spots in Digital Color Fundus Photographs for Diabetic Retinopathy Diagnosis

purpose. To describe and evaluate a machine learning-based, automated system to detect exudates and cotton-wool spots in digital color fundus photographs and differentiate them from drusen, for early diagnosis of diabetic retinopathy. methods. Three hundred retinal images from one eye of 300 patients with diabetes were selected from a diabetic retinopathy telediagnosis database (nonmydriatic camera, two-field photography): 100 with previously diagnosed bright lesions and 200 without. A machine learning computer program was developed that can identify and differentiate among drusen, (hard) exudates, and cotton-wool spots. A human expert standard for the 300 images was obtained by consensus annotation by two retinal specialists. Sensitivities and specificities of the annotations on the 300 images by the automated system and a third retinal specialist were determined. results. The system achieved an area under the receiver operating characteristic (ROC) curve of 0.95 and sensitivity/specificity pairs of 0.95/0.88 for the detection of bright lesions of any type, and 0.95/0.86, 0.70/0.93, and 0.77/0.88 for the detection of exudates, cotton-wool spots, and drusen, respectively. The third retinal specialist achieved pairs of 0.95/0.74 for bright lesions and 0.90/0.98, 0.87/0.98, and 0.92/0.79 per lesion type. conclusions. A machine learning-based, automated system capable of detecting exudates and cotton-wool spots and differentiating them from drusen in color images obtained in community based diabetic patients has been developed and approaches the performance level of retinal experts. If the machine learning can be improved with additional training data sets, it may be useful for detecting clinically important bright lesions, enhancing early diagnosis, and reducing visual loss in patients with diabetes