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Crowdsourcing and Automated Retinal Image Analysis for Diabetic Retinopathy
Lucy I Mudie1, Xueyang Wang1, David S Friedman1
1Wilmer Eye Institute, Johns Hopkins University School of Medicine, 600 N. Wolfe St. Maumenee 711, Baltimore, MD, 21281, USA.
Purpose Of Review:
As the number of people with diabetic retinopathy (DR) in the USA is expected to increase threefold by 2050, the need to reduce health care costs associated with screening for this treatable disease is ever present. Crowdsourcing and automated retinal image analysis (ARIA) are two areas where new technology has been applied to reduce costs in screening for DR. This paper reviews the current literature surrounding these new technologies.
Recent Findings:
Crowdsourcing has high sensitivity for normal vs abnormal images; however, when multiple categories for severity of DR are added, specificity is reduced. ARIAs have higher sensitivity and specificity, and some commercial ARIA programs are already in use. Deep learning enhanced ARIAs appear to offer even more improvement in ARIA grading accuracy. The utilization of crowdsourcing and ARIAs may be a key to reducing the time and cost burden of processing images from DR screening.
