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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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Autores principales: Xu, Fabao, Wan, Cheng, Zhao, Lanqin, Liu, Shaopeng, Hong, Jiaming, Xiang, Yifan, You, Qijing, Zhou, Lijun, Li, Zhongwen, Gong, Songjian, Zhu, Yi, Chen, Chuan, Zhang, Li, Gong, Yajun, Li, Longhui, Li, Cong, Zhang, Xiayin, Guo, Chong, Lai, Kunbei, Huang, Chuangxin, Ting, Daniel, Lin, Haotian, Jin, Chenjin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8650495/
https://www.ncbi.nlm.nih.gov/pubmed/34888298
http://dx.doi.org/10.3389/fbioe.2021.649221
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author Xu, Fabao
Wan, Cheng
Zhao, Lanqin
Liu, Shaopeng
Hong, Jiaming
Xiang, Yifan
You, Qijing
Zhou, Lijun
Li, Zhongwen
Gong, Songjian
Zhu, Yi
Chen, Chuan
Zhang, Li
Gong, Yajun
Li, Longhui
Li, Cong
Zhang, Xiayin
Guo, Chong
Lai, Kunbei
Huang, Chuangxin
Ting, Daniel
Lin, Haotian
Jin, Chenjin
author_facet Xu, Fabao
Wan, Cheng
Zhao, Lanqin
Liu, Shaopeng
Hong, Jiaming
Xiang, Yifan
You, Qijing
Zhou, Lijun
Li, Zhongwen
Gong, Songjian
Zhu, Yi
Chen, Chuan
Zhang, Li
Gong, Yajun
Li, Longhui
Li, Cong
Zhang, Xiayin
Guo, Chong
Lai, Kunbei
Huang, Chuangxin
Ting, Daniel
Lin, Haotian
Jin, Chenjin
author_sort Xu, Fabao
collection PubMed
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.
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spelling pubmed-86504952021-12-08 Predicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence Xu, Fabao Wan, Cheng Zhao, Lanqin Liu, Shaopeng Hong, Jiaming Xiang, Yifan You, Qijing Zhou, Lijun Li, Zhongwen Gong, Songjian Zhu, Yi Chen, Chuan Zhang, Li Gong, Yajun Li, Longhui Li, Cong Zhang, Xiayin Guo, Chong Lai, Kunbei Huang, Chuangxin Ting, Daniel Lin, Haotian Jin, Chenjin Front Bioeng Biotechnol Bioengineering and Biotechnology 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. Frontiers Media S.A. 2021-11-23 /pmc/articles/PMC8650495/ /pubmed/34888298 http://dx.doi.org/10.3389/fbioe.2021.649221 Text en Copyright © 2021 Xu, Wan, Zhao, Liu, Hong, Xiang, You, Zhou, Li, Gong, Zhu, Chen, Zhang, Gong, Li, Li, Zhang, Guo, Lai, Huang, Ting, Lin and Jin. 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
Wan, Cheng
Zhao, Lanqin
Liu, Shaopeng
Hong, Jiaming
Xiang, Yifan
You, Qijing
Zhou, Lijun
Li, Zhongwen
Gong, Songjian
Zhu, Yi
Chen, Chuan
Zhang, Li
Gong, Yajun
Li, Longhui
Li, Cong
Zhang, Xiayin
Guo, Chong
Lai, Kunbei
Huang, Chuangxin
Ting, Daniel
Lin, Haotian
Jin, Chenjin
Predicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence
title 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_short 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
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8650495/
https://www.ncbi.nlm.nih.gov/pubmed/34888298
http://dx.doi.org/10.3389/fbioe.2021.649221
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