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

Eye and Vision

Zekuan Yu¹  Jia Qu²  Qiushi Ren³  Kai Wang⁴  Tao Tang⁴  Yuzhuo Fan⁴  Qiong Xu⁴  Zisu Peng⁴  Mingwei Zhao⁴ 

[1] Academy for Engineering & Technology, Fudan University, Shanghai, China;Department of Biomedical Engineering, College of Engineering, Peking University, 100871, Beijing, China;College of Optometry, Peking University Health Science Center, Beijing, China;School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China;Department of Biomedical Engineering, College of Engineering, Peking University, 100871, Beijing, China;Department of Ophthalmology & Clinical Centre of Optometry, Peking University People’s Hospital, 100044, Beijing, China;College of Optometry, Peking University Health Science Center, Beijing, China;Eye Disease and Optometry Institute, Peking University People’s Hospital, Beijing, China;Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China;

关键词: Myopia;    Myopia progression;    Machine learning;    Ocular axial length;    Physiological elongation;    Orthokeratology;   

DOI  :  10.1186/s40662-020-00214-2

来源: Springer

【 摘 要 】

BackgroundAxial 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.MethodsIn 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 ALpredicted-age curves based on an unchanged SER value with increasing age.ResultsAmong the six algorithms, the robust linear regression model was the best model for predicting AL, with a R2 value of 0.87 and relatively minimal averaged errors between the predicted AL and true AL. Based on the partial derivatives of the ALpredicted-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.ConclusionsThe 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.

【 授权许可】

CC BY