Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning
Gabriella Moraes, Dun Jack Fu, Marc Wilson, Hagar Khalid, Siegfried K. Wagner, Edward Korot, Daniel Ferraz, Livia Faes, Christopher J. Kelly, Terry Spitz, Praveen J. Patel, Konstantinos Balaskas, Tiarnan D. L. Keenan, Pearse A. Keane & Reena Chopra
Purpose: To apply a deep learning algorithm for automated, objective, and comprehensive quantification of optical coherence tomography (OCT) scans to a large real-world dataset of eyes with neovascular age-related macular degeneration (AMD), and make the raw segmentation output data openly available for further research. Design: Retrospective analysis of OCT images from the Moorfields Eye Hospital AMD Database. Participants: 2473 first-treated eyes and another 493 second-treated eyes that commenced therapy for neovascular AMD between June 2012...
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