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A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children
BACKGROUND: Axial myopia is the most common type of myopia. However, due to the high incidence of myopia in Chinese children, few studies estimating the physiological elongation of the ocular axial length (AL), which does not cause myopia progression and differs from the non-physiological elongation...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7579939/ https://www.ncbi.nlm.nih.gov/pubmed/33102610 http://dx.doi.org/10.1186/s40662-020-00214-2 |
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author | Tang, Tao Yu, Zekuan Xu, Qiong Peng, Zisu Fan, Yuzhuo Wang, Kai Ren, Qiushi Qu, Jia Zhao, Mingwei |
author_facet | Tang, Tao Yu, Zekuan Xu, Qiong Peng, Zisu Fan, Yuzhuo Wang, Kai Ren, Qiushi Qu, Jia Zhao, Mingwei |
author_sort | Tang, Tao |
collection | PubMed |
description | BACKGROUND: Axial myopia is the most common type of myopia. However, due to the high incidence of myopia in Chinese children, few studies estimating the physiological elongation of the ocular axial length (AL), which does not cause myopia progression and differs from the non-physiological elongation of AL, have been conducted. The purpose of our study was to construct a machine learning (ML)-based model for estimating the physiological elongation of AL in a sample of Chinese school-aged myopic children. METHODS: In total, 1011 myopic children aged 6 to 18 years participated in this study. Cross-sectional datasets were used to optimize the ML algorithms. The input variables included age, sex, central corneal thickness (CCT), spherical equivalent refractive error (SER), mean K reading (K-mean), and white-to-white corneal diameter (WTW). The output variable was AL. A 5-fold cross-validation scheme was used to randomly divide all data into 5 groups, including 4 groups used as training data and one group used as validation data. Six types of ML algorithms were implemented in our models. The best-performing algorithm was applied to predict AL, and estimates of the physiological elongation of AL were obtained as the partial derivatives of AL(predicted)-age curves based on an unchanged SER value with increasing age. RESULTS: Among the six algorithms, the robust linear regression model was the best model for predicting AL, with a R(2) value of 0.87 and relatively minimal averaged errors between the predicted AL and true AL. Based on the partial derivatives of the AL(predicted)-age curves, the estimated physiological AL elongation varied from 0.010 to 0.116 mm/year in male subjects and 0.003 to 0.110 mm/year in female subjects and was influenced by age, SER and K-mean. According to the model, the physiological elongation of AL linearly decreased with increasing age and was negatively correlated with the SER and the K-mean. CONCLUSIONS: The physiological elongation of the AL is rarely recorded in clinical data in China. In cases of unavailable clinical data, an ML algorithm could provide practitioners a reasonable model that can be used to estimate the physiological elongation of AL, which is especially useful when monitoring myopia progression in orthokeratology lens wearers. |
format | Online Article Text |
id | pubmed-7579939 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75799392020-10-22 A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children Tang, Tao Yu, Zekuan Xu, Qiong Peng, Zisu Fan, Yuzhuo Wang, Kai Ren, Qiushi Qu, Jia Zhao, Mingwei Eye Vis (Lond) Research BACKGROUND: Axial myopia is the most common type of myopia. However, due to the high incidence of myopia in Chinese children, few studies estimating the physiological elongation of the ocular axial length (AL), which does not cause myopia progression and differs from the non-physiological elongation of AL, have been conducted. The purpose of our study was to construct a machine learning (ML)-based model for estimating the physiological elongation of AL in a sample of Chinese school-aged myopic children. METHODS: In total, 1011 myopic children aged 6 to 18 years participated in this study. Cross-sectional datasets were used to optimize the ML algorithms. The input variables included age, sex, central corneal thickness (CCT), spherical equivalent refractive error (SER), mean K reading (K-mean), and white-to-white corneal diameter (WTW). The output variable was AL. A 5-fold cross-validation scheme was used to randomly divide all data into 5 groups, including 4 groups used as training data and one group used as validation data. Six types of ML algorithms were implemented in our models. The best-performing algorithm was applied to predict AL, and estimates of the physiological elongation of AL were obtained as the partial derivatives of AL(predicted)-age curves based on an unchanged SER value with increasing age. RESULTS: Among the six algorithms, the robust linear regression model was the best model for predicting AL, with a R(2) value of 0.87 and relatively minimal averaged errors between the predicted AL and true AL. Based on the partial derivatives of the AL(predicted)-age curves, the estimated physiological AL elongation varied from 0.010 to 0.116 mm/year in male subjects and 0.003 to 0.110 mm/year in female subjects and was influenced by age, SER and K-mean. According to the model, the physiological elongation of AL linearly decreased with increasing age and was negatively correlated with the SER and the K-mean. CONCLUSIONS: The physiological elongation of the AL is rarely recorded in clinical data in China. In cases of unavailable clinical data, an ML algorithm could provide practitioners a reasonable model that can be used to estimate the physiological elongation of AL, which is especially useful when monitoring myopia progression in orthokeratology lens wearers. BioMed Central 2020-10-22 /pmc/articles/PMC7579939/ /pubmed/33102610 http://dx.doi.org/10.1186/s40662-020-00214-2 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Tang, Tao Yu, Zekuan Xu, Qiong Peng, Zisu Fan, Yuzhuo Wang, Kai Ren, Qiushi Qu, Jia Zhao, Mingwei A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children |
title | A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children |
title_full | A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children |
title_fullStr | A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children |
title_full_unstemmed | A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children |
title_short | A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children |
title_sort | machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7579939/ https://www.ncbi.nlm.nih.gov/pubmed/33102610 http://dx.doi.org/10.1186/s40662-020-00214-2 |
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