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

作者：Tao Tang, Zekuan Yu, Qiong Xu, Zisu Peng, Yuzhuo Fan, Kai Wang, Qiushi Ren, Jia Qu, Mingwei Zhao · 发表于：Eye and Vision · 年份：2020 · DOI：10.1186/s40662-020-00214-2 · 被引用次数：66 · 研究领域：Ophthalmology and Visual Impairment Studies、Corneal surgery and disorders、Retinopathy of Prematurity Studies

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...