Abstract
In this chapter, we focus on a series of collaborations between clinicians from the School of Medicine and computer scientists from the Applied Physics Lab at the Johns Hopkins University. The described studies utilized deep learning (DL) and focused on analysis of color fundus photographs (CFP) of patients with age-related macular degeneration (AMD), the leading cause of central vision loss in persons over age 50 in the United States [1] and around the world. The dataset used for training and testing was derived from the Age-Related Eye Disease Studies (AREDS) [2], a longitudinal cohort study funded by the National Eye Institute with over 4500 participants and roughly 130,000 CFPs taken with a 30 degree camera. The ground truth of the deep learning systems (DLS) was based on the annotations (gradings) by trained graders at the University of Wisconsin Fundus Photograph Reading Center, which is the designated reading center for the AREDS.
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Alvin Liu, T.Y., Bressler, N.M. (2021). Automatic Retinal Imaging and Analysis: Age-Related Macular Degeneration (AMD) within Age-Related Eye Disease Studies (AREDS). In: Grzybowski, A. (eds) Artificial Intelligence in Ophthalmology. Springer, Cham. https://doi.org/10.1007/978-3-030-78601-4_14
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