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Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network
  1. Yutong Liu1,2,
  2. Jingyuan Yang1,2,
  3. Yang Zhou3,
  4. Weisen Wang4,
  5. Jianchun Zhao3,
  6. Weihong Yu1,2,
  7. Dingding Zhang5,
  8. Dayong Ding3,
  9. Xirong Li4,
  10. Youxin Chen1,2
  1. 1 Ophthalmology, Peking Union Medical College Hospital, Beijing, China
  2. 2 Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Beijing, China
  3. 3 Vistel AI Lab, Visionary Intelligence Ltd, Beijing, China
  4. 4 Key Lab of DEKE, Renmin University of China, Beijing, China
  5. 5 Central Research Laboratory, Peking Union Medical College Hospital, Beijing, China
  1. Correspondence to Dr Weihong Yu, Ophthalmology, Peking Union Medical College Hospital, Beijing 100730, China; yuwh{at}pumch.cn; Professor Youxin Chen, Ophthalmology, Peking Union Medical College Hospital, Beijing, China; chenyx{at}pumch.cn

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.

  • retina

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Footnotes

  • YL and JY are joint first authors.

  • Correction notice This paper has been updated since it was published online. The affiliation, Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Beijing, China, was missed for the following authors, Yutong Liu, Jingyuan Yang, Weihong Yu, and Youxin Chen.

  • Contributors YL and JY proposed the idea, designed the study, collected images, evaluated images and wrote both the original and revised manuscript together. They contributed equally to these parts. YZ, WW and JZ were incharge of AI training procedure and participated in writing the AI section in Method part of the manuscript under the guidance of DD and XL. DZ participated in statistical analysis. WY and YC helped with study design and the writing of the manuscripts, and the conduction of this study is supervised by them.

  • Funding Chinese Academy of Medical Sciences Initiative for Innovative Medicine (CAMS-I2M, 2018-I2M-AI 001). Pharmaceutical collaborative innovation project of Beijing Science and Technology Commission (Z191100007719002). National Key Research and Development Project (SQ2018YFC200148). National Natural Science Foundation of China (NSFC) (81670879). Beijing Natural Science Foundation (4202033)

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval The Institutional Review Board of Peking Union Medical College Hospital approved this retrospective study (No. S-K631). The study followed the tenets of the Declaration of Helsinki.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available on reasonable request.

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