1. Post-treatment prediction of optical coherence tomography using a conditional generative adversarial network in age-related macular degeneration

    Post-treatment prediction of optical coherence tomography using a conditional generative adversarial network in age-related macular degeneration

    Purpose: To develop a deep learning model to generate post-treatment optical coherence tomography (OCT) images of neovascular age-related macular degeneration (nAMD). Methods: Two hundred ninety-eight patients with nAMD were included. The conditional generative adversarial network (cGAN) was trained using 15183 augmented paired OCT B-scan images obtained from 723 scans of 241 patients at baseline and 1 month after 3 loading doses of an anti-vascular endothelial growth factor (VEGF) treatment. The network was also trained using baseline fluorescein angiography (FA) or indocyanine green angiography (ICGA) images together with baseline OCT images. A test set of 150 images of 50 eyes was ...

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