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Longtime Vision Function Prediction in Childhood Cataract Patients Based on Optical Coherence Tomography Images

The results of visual prediction reflect the tendency and speed of visual development during a future period, based on which ophthalmologists and guardians can know the potential visual prognosis in advance, decide on an intervention plan, and contribute to visual development. In our study, we devel...

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Autores principales: Xiang, Yifan, Chen, Jingjing, Xu, Fabao, Lin, Zhuoling, Xiao, Jun, Lin, Zhenzhe, Lin, Haotian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7973224/
https://www.ncbi.nlm.nih.gov/pubmed/33748090
http://dx.doi.org/10.3389/fbioe.2021.646479
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author Xiang, Yifan
Chen, Jingjing
Xu, Fabao
Lin, Zhuoling
Xiao, Jun
Lin, Zhenzhe
Lin, Haotian
author_facet Xiang, Yifan
Chen, Jingjing
Xu, Fabao
Lin, Zhuoling
Xiao, Jun
Lin, Zhenzhe
Lin, Haotian
author_sort Xiang, Yifan
collection PubMed
description The results of visual prediction reflect the tendency and speed of visual development during a future period, based on which ophthalmologists and guardians can know the potential visual prognosis in advance, decide on an intervention plan, and contribute to visual development. In our study, we developed an intelligent system based on the features of optical coherence tomography images for long-term prediction of best corrected visual acuity (BCVA) 3 and 5 years in advance. Two hundred eyes of 132 patients were included. Six machine learning algorithms were applied. In the BCVA predictions, small errors within two lines of the visual chart were achieved. The mean absolute errors (MAEs) between the prediction results and ground truth were 0.1482–0.2117 logMAR for 3-year predictions and 0.1198–0.1845 logMAR for 5-year predictions; the root mean square errors (RMSEs) were 0.1916–0.2942 logMAR for 3-year predictions and 0.1692–0.2537 logMAR for 5-year predictions. This is the first study to predict post-therapeutic BCVAs in young children. This work establishes a reliable method to predict prognosis 5 years in advance. The application of our research contributes to the design of visual intervention plans and visual prognosis.
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spelling pubmed-79732242021-03-20 Longtime Vision Function Prediction in Childhood Cataract Patients Based on Optical Coherence Tomography Images Xiang, Yifan Chen, Jingjing Xu, Fabao Lin, Zhuoling Xiao, Jun Lin, Zhenzhe Lin, Haotian Front Bioeng Biotechnol Bioengineering and Biotechnology The results of visual prediction reflect the tendency and speed of visual development during a future period, based on which ophthalmologists and guardians can know the potential visual prognosis in advance, decide on an intervention plan, and contribute to visual development. In our study, we developed an intelligent system based on the features of optical coherence tomography images for long-term prediction of best corrected visual acuity (BCVA) 3 and 5 years in advance. Two hundred eyes of 132 patients were included. Six machine learning algorithms were applied. In the BCVA predictions, small errors within two lines of the visual chart were achieved. The mean absolute errors (MAEs) between the prediction results and ground truth were 0.1482–0.2117 logMAR for 3-year predictions and 0.1198–0.1845 logMAR for 5-year predictions; the root mean square errors (RMSEs) were 0.1916–0.2942 logMAR for 3-year predictions and 0.1692–0.2537 logMAR for 5-year predictions. This is the first study to predict post-therapeutic BCVAs in young children. This work establishes a reliable method to predict prognosis 5 years in advance. The application of our research contributes to the design of visual intervention plans and visual prognosis. Frontiers Media S.A. 2021-03-05 /pmc/articles/PMC7973224/ /pubmed/33748090 http://dx.doi.org/10.3389/fbioe.2021.646479 Text en Copyright © 2021 Xiang, Chen, Xu, Lin, Xiao, Lin and Lin. http://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
Xiang, Yifan
Chen, Jingjing
Xu, Fabao
Lin, Zhuoling
Xiao, Jun
Lin, Zhenzhe
Lin, Haotian
Longtime Vision Function Prediction in Childhood Cataract Patients Based on Optical Coherence Tomography Images
title Longtime Vision Function Prediction in Childhood Cataract Patients Based on Optical Coherence Tomography Images
title_full Longtime Vision Function Prediction in Childhood Cataract Patients Based on Optical Coherence Tomography Images
title_fullStr Longtime Vision Function Prediction in Childhood Cataract Patients Based on Optical Coherence Tomography Images
title_full_unstemmed Longtime Vision Function Prediction in Childhood Cataract Patients Based on Optical Coherence Tomography Images
title_short Longtime Vision Function Prediction in Childhood Cataract Patients Based on Optical Coherence Tomography Images
title_sort longtime vision function prediction in childhood cataract patients based on optical coherence tomography images
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7973224/
https://www.ncbi.nlm.nih.gov/pubmed/33748090
http://dx.doi.org/10.3389/fbioe.2021.646479
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