title: Automated detection of retinal disease.
abstract: Nearly 4 in 10 Americans with diabetes currently fail to undergo recommended annual retinal exams, resulting in tens of thousands of cases of blindness that could have been prevented. Advances in automated retinal disease detection could greatly reduce the burden of labor-intensive dilated retinal examinations by ophthalmologists and optometrists and deliver diagnostic services at lower cost. As the current availability of ophthalmologists and optometrists is inadequate to screen all patients at risk every year, automated screening systems deployed in primary care settings and even in patients' homes could fill the current gap in supply. Expanding screens to all patients at risk by switching to automated detection systems would in turn yield significantly higher rates of detecting and treating diabetic retinopathy per dilated retinal examination. Fewer diabetic patients would develop complications such as blindness, while ophthalmologists could focus on more complex cases.
authors: Department of Health Administration and Policy, George Mason University, 4400 University Dr - MS: 1J3, Fairfax, VA 22030. E-mail: lhelmche@gmu.edu.
authors: Lorens A
authors: LA
authors: Helmchen
authors: Harold P
authors: HP
authors: Lehmann
authors: Michael D
authors: MD
authors: Abràmoff
mesh_terms: D003930:Diabetic Retinopathy / Q000175:diagnosis*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D008403:Mass Screening / Q000295:instrumentation*; D019095:Point-of-Care Systems; D012164:Retinal Diseases / Q000175:diagnosis; D012680:Sensitivity and Specificity