Predicting incremental and future visual change in neovascular age-related macular degeneration using deep learning
Version 1Purpose To evaluate the predictive utility of quantitative imaging biomarkers, acquired automatically from optical coherence tomography (OCT) scans, of cross-sectional and future visual outcomes of patients with neovascular age-related macular degeneration (AMD) starting anti-vascular endothelial growth factor (VEGF) therapy. Design Retrospective cohort study. Methods Automatic segmentation was carried out by applying a deep learning segmentation algorithm to 137,379 OCT scans from 6467 ey
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Purpose To evaluate the predictive utility of quantitative imaging biomarkers, acquired automatically from optical coherence tomography (OCT) scans, of cross-sectional and future visual outcomes of patients with neovascular age-related macular degeneration (AMD) starting anti-vascular endothelial growth factor (VEGF) therapy. Design Retrospective cohort study. Methods Automatic segmentation was carried out by applying a deep learning segmentation algorithm to 137,379 OCT scans from 6467 ey