Dong-Wouk Park1,2, Steven L Mansberger1,2,3. 1. 1 Legacy Devers Eye Institute , Legacy Health, Portland, Oregon. 2. 2 Casey Eye Institute, Oregon Health and Science University , Portland, Oregon. 3. 3 Department of Public Health and Preventive Medicine, Oregon Health and Science University , Portland, Oregon.
Abstract
BACKGROUND: Telemedicine with nonmydriatic cameras can detect not only diabetic retinopathy but also other eye disease. OBJECTIVE: To determine the prevalence of eye diseases detected by telemedicine in a population with a high prevalence of minority and American Indian/Alaskan Native (AI/AN) ethnicities. SUBJECTS AND METHODS: We recruited diabetic patients 18 years and older and used telemedicine with nonmydriatic cameras to detect eye disease. Two trained readers graded the images for diabetic retinopathy, age-related macular degeneration (ARMD), glaucomatous features, macular edema, and other eye disease using a standard protocol. We included both eyes for analysis and excluded images that were too poor to grade. RESULTS: We included 820 eyes from 424 patients with 72.3% nonwhite ethnicity and 50.3% AI/AN heritage. While 283/424 (66.7%) patients had normal eye images, 120/424 (28.3%) had one disease identified; 15/424 (3.5%) had two diseases; and 6/424 (1.4%) had three diseases in one or both eyes. After diabetic retinopathy (104/424, 24.5%), the most common eye diseases were glaucomatous features (44/424, 10.4%) and dry ARMD (24/424, 5.7%). Seventeen percent (72/424, 17.0%) showed eye disease other than diabetic retinopathy. CONCLUSIONS: Telemedicine with nonmydriatic cameras detected diabetic retinopathy, as well as other visually significant eye disease. This suggests that a diabetic retinopathy screening program needs to detect and report other eye disease, including glaucoma and macular disease.
BACKGROUND: Telemedicine with nonmydriatic cameras can detect not only diabetic retinopathy but also other eye disease. OBJECTIVE: To determine the prevalence of eye diseases detected by telemedicine in a population with a high prevalence of minority and American Indian/Alaskan Native (AI/AN) ethnicities. SUBJECTS AND METHODS: We recruited diabeticpatients 18 years and older and used telemedicine with nonmydriatic cameras to detect eye disease. Two trained readers graded the images for diabetic retinopathy, age-related macular degeneration (ARMD), glaucomatous features, macular edema, and other eye disease using a standard protocol. We included both eyes for analysis and excluded images that were too poor to grade. RESULTS: We included 820 eyes from 424 patients with 72.3% nonwhite ethnicity and 50.3% AI/AN heritage. While 283/424 (66.7%) patients had normal eye images, 120/424 (28.3%) had one disease identified; 15/424 (3.5%) had two diseases; and 6/424 (1.4%) had three diseases in one or both eyes. After diabetic retinopathy (104/424, 24.5%), the most common eye diseases were glaucomatous features (44/424, 10.4%) and dry ARMD (24/424, 5.7%). Seventeen percent (72/424, 17.0%) showed eye disease other than diabetic retinopathy. CONCLUSIONS: Telemedicine with nonmydriatic cameras detected diabetic retinopathy, as well as other visually significant eye disease. This suggests that a diabetic retinopathy screening program needs to detect and report other eye disease, including glaucoma and macular disease.
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