Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning
Version 1Purpose: 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:
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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: