Predicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence
To predict visual acuity (VA) and post-therapeutic optical coherence tomography (OCT) images 1, 3, and 6 months after laser treatment in patients with central serous chorioretinopathy (CSC) by artificial intelligence (AI). Real-world clinical and imaging data were collected at Zhongshan Ophthalmic C...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:77043c3c317c453187143675f523cdb32021-11-30T13:41:09ZPredicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence2296-418510.3389/fbioe.2021.649221https://doaj.org/article/77043c3c317c453187143675f523cdb32021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fbioe.2021.649221/fullhttps://doaj.org/toc/2296-4185To predict visual acuity (VA) and post-therapeutic optical coherence tomography (OCT) images 1, 3, and 6 months after laser treatment in patients with central serous chorioretinopathy (CSC) by artificial intelligence (AI). Real-world clinical and imaging data were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The data obtained from ZOC (416 eyes of 401 patients) were used as the training set; the data obtained from XEC (64 eyes of 60 patients) were used as the test set. Six different machine learning algorithms and a blending algorithm were used to predict VA, and a pix2pixHD method was adopted to predict post-therapeutic OCT images in patients after laser treatment. The data for VA predictions included clinical features obtained from electronic medical records (20 features) and measured features obtained from fundus fluorescein angiography, indocyanine green angiography, and OCT (145 features). The data for OCT predictions included 480 pairs of pre- and post-therapeutic OCT images. The VA and OCT images predicted by AI were compared with the ground truth. In the VA predictions of XEC dataset, the mean absolute errors (MAEs) were 0.074–0.098 logMAR (within four to five letters), and the root mean square errors were 0.096–0.127 logMAR (within five to seven letters) for the 1-, 3-, and 6-month predictions, respectively; in the post-therapeutic OCT predictions, only about 5.15% (5 of 97) of synthetic OCT images could be accurately identified as synthetic images. The MAEs of central macular thickness of synthetic OCT images were 30.15 ± 13.28 μm and 22.46 ± 9.71 μm for the 1- and 3-month predictions, respectively. This is the first study to apply AI to predict VA and post-therapeutic OCT of patients with CSC. This work establishes a reliable method of predicting prognosis 6 months in advance; the application of AI has the potential to help reduce patient anxiety and serve as a reference for ophthalmologists when choosing optimal laser treatments.Fabao XuCheng WanLanqin ZhaoShaopeng LiuJiaming HongYifan XiangQijing YouLijun ZhouZhongwen LiSongjian GongYi ZhuChuan ChenLi ZhangLi ZhangYajun GongLonghui LiCong LiXiayin ZhangChong GuoKunbei LaiChuangxin HuangDaniel TingHaotian LinHaotian LinChenjin JinFrontiers Media S.A.articleartificial intelligencemachine learningcentral serous chorioretinopathyvisual acuityoptical coherence tomographyBiotechnologyTP248.13-248.65ENFrontiers in Bioengineering and Biotechnology, Vol 9 (2021) |
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| topic |
artificial intelligence machine learning central serous chorioretinopathy visual acuity optical coherence tomography Biotechnology TP248.13-248.65 |
| spellingShingle |
artificial intelligence machine learning central serous chorioretinopathy visual acuity optical coherence tomography Biotechnology TP248.13-248.65 Fabao Xu Cheng Wan Lanqin Zhao Shaopeng Liu Jiaming Hong Yifan Xiang Qijing You Lijun Zhou Zhongwen Li Songjian Gong Yi Zhu Chuan Chen Li Zhang Li Zhang Yajun Gong Longhui Li Cong Li Xiayin Zhang Chong Guo Kunbei Lai Chuangxin Huang Daniel Ting Haotian Lin Haotian Lin Chenjin Jin Predicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence |
| description |
To predict visual acuity (VA) and post-therapeutic optical coherence tomography (OCT) images 1, 3, and 6 months after laser treatment in patients with central serous chorioretinopathy (CSC) by artificial intelligence (AI). Real-world clinical and imaging data were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The data obtained from ZOC (416 eyes of 401 patients) were used as the training set; the data obtained from XEC (64 eyes of 60 patients) were used as the test set. Six different machine learning algorithms and a blending algorithm were used to predict VA, and a pix2pixHD method was adopted to predict post-therapeutic OCT images in patients after laser treatment. The data for VA predictions included clinical features obtained from electronic medical records (20 features) and measured features obtained from fundus fluorescein angiography, indocyanine green angiography, and OCT (145 features). The data for OCT predictions included 480 pairs of pre- and post-therapeutic OCT images. The VA and OCT images predicted by AI were compared with the ground truth. In the VA predictions of XEC dataset, the mean absolute errors (MAEs) were 0.074–0.098 logMAR (within four to five letters), and the root mean square errors were 0.096–0.127 logMAR (within five to seven letters) for the 1-, 3-, and 6-month predictions, respectively; in the post-therapeutic OCT predictions, only about 5.15% (5 of 97) of synthetic OCT images could be accurately identified as synthetic images. The MAEs of central macular thickness of synthetic OCT images were 30.15 ± 13.28 μm and 22.46 ± 9.71 μm for the 1- and 3-month predictions, respectively. This is the first study to apply AI to predict VA and post-therapeutic OCT of patients with CSC. This work establishes a reliable method of predicting prognosis 6 months in advance; the application of AI has the potential to help reduce patient anxiety and serve as a reference for ophthalmologists when choosing optimal laser treatments. |
| format |
article |
| author |
Fabao Xu Cheng Wan Lanqin Zhao Shaopeng Liu Jiaming Hong Yifan Xiang Qijing You Lijun Zhou Zhongwen Li Songjian Gong Yi Zhu Chuan Chen Li Zhang Li Zhang Yajun Gong Longhui Li Cong Li Xiayin Zhang Chong Guo Kunbei Lai Chuangxin Huang Daniel Ting Haotian Lin Haotian Lin Chenjin Jin |
| author_facet |
Fabao Xu Cheng Wan Lanqin Zhao Shaopeng Liu Jiaming Hong Yifan Xiang Qijing You Lijun Zhou Zhongwen Li Songjian Gong Yi Zhu Chuan Chen Li Zhang Li Zhang Yajun Gong Longhui Li Cong Li Xiayin Zhang Chong Guo Kunbei Lai Chuangxin Huang Daniel Ting Haotian Lin Haotian Lin Chenjin Jin |
| author_sort |
Fabao Xu |
| title |
Predicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence |
| title_short |
Predicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence |
| title_full |
Predicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence |
| title_fullStr |
Predicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence |
| title_full_unstemmed |
Predicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence |
| title_sort |
predicting post-therapeutic visual acuity and oct images in patients with central serous chorioretinopathy by artificial intelligence |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/77043c3c317c453187143675f523cdb3 |
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