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...
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2022.914964/full |
| _version_ | 1811239361588494336 |
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| author | Fabao Xu Xuechen Yu Yang Gao Yang Gao Xiaolin Ning Xiaolin Ning Ziyuan Huang Min Wei Weibin Zhai Rui Zhang Shaopeng Wang Jianqiao Li |
| author_facet | Fabao Xu Xuechen Yu Yang Gao Yang Gao Xiaolin Ning Xiaolin Ning Ziyuan Huang Min Wei Weibin Zhai Rui Zhang Shaopeng Wang Jianqiao Li |
| author_sort | Fabao Xu |
| collection | DOAJ |
| 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. |
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| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-04-12T12:58:30Z |
| publishDate | 2022-10-01 |
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| spelling | doaj.art-bbf54b777a3544f68b06346d5283d2d72022-12-22T03:32:15ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852022-10-011010.3389/fbioe.2022.914964914964Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial networkFabao Xu0Xuechen Yu1Yang Gao2Yang Gao3Xiaolin Ning4Xiaolin Ning5Ziyuan Huang6Min Wei7Weibin Zhai8Rui Zhang9Shaopeng Wang10Jianqiao Li11Department of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaSchool of Physics, Beihang University, Beijing, ChinaHangzhou Innovation Institute, Beihang University, Hangzhou, ChinaHangzhou Innovation Institute, Beihang University, Hangzhou, ChinaResearch Institute of Frontier Science, Beihang University, Beijing, ChinaResearch Institute of Frontier Science, Beihang University, Beijing, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaDepartment of Ophthalmology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaTo 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.https://www.frontiersin.org/articles/10.3389/fbioe.2022.914964/fulldeep learningretinal vein occlusiongenerative adversarial networksoptical coherence tomographyartificial intelligence |
| spellingShingle | Fabao Xu Xuechen Yu Yang Gao Yang Gao Xiaolin Ning Xiaolin Ning Ziyuan Huang Min Wei Weibin Zhai Rui Zhang Shaopeng Wang Jianqiao Li Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network Frontiers in Bioengineering and Biotechnology deep learning retinal vein occlusion generative adversarial networks optical coherence tomography artificial intelligence |
| 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 | deep learning retinal vein occlusion generative adversarial networks optical coherence tomography artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2022.914964/full |
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