A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children

Tao Tang; Zekuan Yu; Qiong Xu; Zisu Peng; Yuzhuo Fan; Kai Wang; Qiushi Ren; Jia Qu; Mingwei Zhao

doi:10.1186/s40662-020-00214-2

A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children

Eye Vis (Lond). 2020 Oct 22:7:50. doi: 10.1186/s40662-020-00214-2. eCollection 2020.

Authors

Tao Tang^#^{1

2

3

4}, Zekuan Yu^#^{5

6}, Qiong Xu^{1

2

3

4}, Zisu Peng^{1

2

3

4}, Yuzhuo Fan^{1

2

3

4}, Kai Wang^{1

2

3

4}, Qiushi Ren⁶, Jia Qu^{2

7}, Mingwei Zhao^{1

2

3

4}

Affiliations

¹ Department of Ophthalmology & Clinical Centre of Optometry, Peking University People's Hospital, Beijing, 100044 China.
² College of Optometry, Peking University Health Science Center, Beijing, China.
³ Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China.
⁴ Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China.
⁵ Academy for Engineering & Technology, Fudan University, Shanghai, China.
⁶ Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871 China.
⁷ School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang China.

^# Contributed equally.

Abstract

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.

Keywords: Machine learning; Myopia; Myopia progression; Ocular axial length; Orthokeratology; Physiological elongation.