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Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network
To generate and evaluate post-therapeutic optical coherence tomography (OCT) images based on pre-therapeutic images with generative adversarial network (GAN) to predict the short-term response of patients with retinal vein occlusion (RVO) to anti-vascular endothelial growth factor (anti-VEGF) therap...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9596772/ https://www.ncbi.nlm.nih.gov/pubmed/36312556 http://dx.doi.org/10.3389/fbioe.2022.914964 |
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author | Xu, Fabao Yu, Xuechen Gao, Yang Ning, Xiaolin Huang, Ziyuan Wei, Min Zhai, Weibin Zhang, Rui Wang, Shaopeng Li, Jianqiao |
author_facet | Xu, Fabao Yu, Xuechen Gao, Yang Ning, Xiaolin Huang, Ziyuan Wei, Min Zhai, Weibin Zhang, Rui Wang, Shaopeng Li, Jianqiao |
author_sort | Xu, Fabao |
collection | PubMed |
description | To generate and evaluate post-therapeutic optical coherence tomography (OCT) images based on pre-therapeutic images with generative adversarial network (GAN) to predict the short-term response of patients with retinal vein occlusion (RVO) to anti-vascular endothelial growth factor (anti-VEGF) therapy. Real-world imaging data were retrospectively collected from 1 May 2017, to 1 June 2021. A total of 515 pairs of pre-and post-therapeutic OCT images of patients with RVO were included in the training set, while 68 pre-and post-therapeutic OCT images were included in the validation set. A pix2pixHD method was adopted to predict post-therapeutic OCT images in RVO patients after anti-VEGF therapy. The quality and similarity of synthetic OCT images were evaluated by screening and evaluation experiments. We quantitatively and qualitatively assessed the prognostic accuracy of the synthetic post-therapeutic OCT images. The post-therapeutic OCT images generated by the pix2pixHD algorithm were comparable to the actual images in edema resorption response. Retinal specialists found most synthetic images (62/68) difficult to differentiate from the real ones. The mean absolute error (MAE) of the central macular thickness (CMT) between the synthetic and real OCT images was 26.33 ± 15.81 μm. There was no statistical difference in CMT between the synthetic and the real images. In this retrospective study, the application of the pix2pixHD algorithm objectively predicted the short-term response of each patient to anti-VEGF therapy based on OCT images with high accuracy, suggestive of its clinical value, especially for screening patients with relatively poor prognosis and potentially guiding clinical treatment. Importantly, our artificial intelligence-based prediction approach’s non-invasiveness, repeatability, and cost-effectiveness can improve compliance and follow-up management of this patient population. |
format | Online Article Text |
id | pubmed-9596772 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95967722022-10-27 Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network Xu, Fabao Yu, Xuechen Gao, Yang Ning, Xiaolin Huang, Ziyuan Wei, Min Zhai, Weibin Zhang, Rui Wang, Shaopeng Li, Jianqiao Front Bioeng Biotechnol Bioengineering and Biotechnology To generate and evaluate post-therapeutic optical coherence tomography (OCT) images based on pre-therapeutic images with generative adversarial network (GAN) to predict the short-term response of patients with retinal vein occlusion (RVO) to anti-vascular endothelial growth factor (anti-VEGF) therapy. Real-world imaging data were retrospectively collected from 1 May 2017, to 1 June 2021. A total of 515 pairs of pre-and post-therapeutic OCT images of patients with RVO were included in the training set, while 68 pre-and post-therapeutic OCT images were included in the validation set. A pix2pixHD method was adopted to predict post-therapeutic OCT images in RVO patients after anti-VEGF therapy. The quality and similarity of synthetic OCT images were evaluated by screening and evaluation experiments. We quantitatively and qualitatively assessed the prognostic accuracy of the synthetic post-therapeutic OCT images. The post-therapeutic OCT images generated by the pix2pixHD algorithm were comparable to the actual images in edema resorption response. Retinal specialists found most synthetic images (62/68) difficult to differentiate from the real ones. The mean absolute error (MAE) of the central macular thickness (CMT) between the synthetic and real OCT images was 26.33 ± 15.81 μm. There was no statistical difference in CMT between the synthetic and the real images. In this retrospective study, the application of the pix2pixHD algorithm objectively predicted the short-term response of each patient to anti-VEGF therapy based on OCT images with high accuracy, suggestive of its clinical value, especially for screening patients with relatively poor prognosis and potentially guiding clinical treatment. Importantly, our artificial intelligence-based prediction approach’s non-invasiveness, repeatability, and cost-effectiveness can improve compliance and follow-up management of this patient population. Frontiers Media S.A. 2022-10-12 /pmc/articles/PMC9596772/ /pubmed/36312556 http://dx.doi.org/10.3389/fbioe.2022.914964 Text en Copyright © 2022 Xu, Yu, Gao, Ning, Huang, Wei, Zhai, Zhang, Wang and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Xu, Fabao Yu, Xuechen Gao, Yang Ning, Xiaolin Huang, Ziyuan Wei, Min Zhai, Weibin Zhang, Rui Wang, Shaopeng Li, Jianqiao Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network |
title | Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network |
title_full | Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network |
title_fullStr | Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network |
title_full_unstemmed | Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network |
title_short | Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network |
title_sort | predicting oct images of short-term response to anti-vegf treatment for retinal vein occlusion using generative adversarial network |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9596772/ https://www.ncbi.nlm.nih.gov/pubmed/36312556 http://dx.doi.org/10.3389/fbioe.2022.914964 |
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