Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network
Yutong Liu, Jingyuan Yang, Yang Zhou, Weisen Wang, Jianchun Zhao, Weihong Yu, Dingding Zhang, Dayong Ding, Xirong Li, Youxin Chen
Abstract
BACKGROUND/AIMS: The aim of this study was to generate and evaluate individualised post-therapeutic optical coherence tomography (OCT) images that could predict the short-term response of antivascular endothelial growth factor therapy for typical neovascular age-related macular degeneration (nAMD) based on pretherapeutic images using generative adversarial network (GAN). METHODS: A total of 476 pairs of pretherapeutic and post-therapeutic OCT images of patients with nAMD were included in training set, while 50 pretherapeutic OCT images were included in the tests set retrospectively, and their corresponding post-therapeutic OCT images were used to evaluate the synthetic images. The pix2pixHD method was adopted for image synthesis. Three experiments were performed to evaluate the quality, authenticity and predictive power of the synthetic images by retinal specialists. RESULTS: We found that 92% of the synthetic OCT images had sufficient quality for further clinical interpretation. Only about 26%-30% synthetic post-therapeutic images could be accurately identified as synthetic images. The accuracy to predict macular status of wet or dry was 0.85 (95% CI 0.74 to 0.95). CONCLUSION: Our results revealed a great potential of GAN to generate post-therapeutic OCT images with both good quality and high accuracy.