RT Journal Article
SR Electronic
T1 Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network
JF British Journal of Ophthalmology
JO Br J Ophthalmol
FD BMJ Publishing Group Ltd.
SP 1735
OP 1740
DO 10.1136/bjophthalmol-2019-315338
VO 104
IS 12
A1 Yutong Liu
A1 Jingyuan Yang
A1 Yang Zhou
A1 Weisen Wang
A1 Jianchun Zhao
A1 Weihong Yu
A1 Dingding Zhang
A1 Dayong Ding
A1 Xirong Li
A1 Youxin Chen
YR 2020
UL http://bjo.bmj.com/content/104/12/1735.abstract
AB 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.