Tang Tao, Yu Zekuan, Xu Qiong, Peng Zisu, Fan Yuzhuo, Wang Kai, Ren Qiushi, Qu Jia, Zhao Mingwei
Department of Ophthalmology & Clinical Centre of Optometry, Peking University People's Hospital, Beijing, 100044 China.
College of Optometry, Peking University Health Science Center, Beijing, China.
Eye Vis (Lond). 2020 Oct 22;7:50. doi: 10.1186/s40662-020-00214-2. eCollection 2020.
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.
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 -age curves based on an unchanged SER value with increasing age.
Among the six algorithms, the robust linear regression model was the best model for predicting AL, with a value of 0.87 and relatively minimal averaged errors between the predicted AL and true AL. Based on the partial derivatives of the -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.
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.
轴性近视是最常见的近视类型。然而，由于中国儿童近视发病率高，很少有研究对不会导致近视进展且与非生理性眼轴长度（AL）伸长不同的生理性AL伸长进行评估。本研究的目的是构建一个基于机器学习（ML）的模型，用于估计中国学龄近视儿童样本中的生理性AL伸长。
共有1011名6至18岁的近视儿童参与了本研究。横断面数据集用于优化ML算法。输入变量包括年龄、性别、中央角膜厚度（CCT）、等效球镜度（SER）、平均角膜曲率读数（K均值）和角膜白对白直径（WTW）。输出变量是AL。采用五折交叉验证方案将所有数据随机分为5组，包括4组用作训练数据和1组用作验证数据。在我们的模型中实施了六种ML算法。应用性能最佳的算法预测AL，并根据年龄曲线的偏导数，在SER值不变且年龄增加的情况下，获得AL生理性伸长的估计值。
在六种算法中,稳健线性回归模型是预测AL的最佳模型，R值为0.87，预测AL与真实AL之间的平均误差相对最小。根据年龄曲线的偏导数，男性受试者估计的生理性AL伸长为0.010至0.116毫米/年，女性受试者为0.003至0.110毫米/年，且受年龄、SER和K均值影响。根据该模型，AL的生理性伸长随年龄增加呈线性下降，且与SER和K均值呈负相关。
在中国临床数据中很少记录AL的生理性伸长。在缺乏临床数据的情况下，ML算法可以为从业者提供一个合理的模型，用于估计AL的生理性伸长，这在监测角膜塑形镜佩戴者的近视进展时特别有用。
