Machine learning-based prediction of orthokeratology therapeutic outcomes in pediatric patients
10.3980/j.issn.1672-5123.2026.8.32
- VernacularTitle:机器学习模型预测青少年近视患者配戴角膜塑形镜的效果
- Author:
Jinju CONG
1
;
Zhimin ZHOU
1
;
Hui SUN
1
Author Information
1. Qianjiang Aier Eye Hospital, Qianjiang 433100, Hubei Province, China
- Publication Type:Journal Article
- Keywords:
orthokeratology;
machine learning;
myopia
- From:
International Eye Science
2026;26(8):1496-1500
- CountryChina
- Language:Chinese
-
Abstract:
AIM: To explore the application value of machine learning models in predicting the myopia control efficacy of orthokeratology(OK)in pediatric population, and analyze the key factors affecting axial length(AL)elongation.METHODS: This retrospective study included myopic subjects aged 8-17 y who were fitted with OK lenses and completed a 6 mo follow-up between January 1, 2021 and December 1, 2023. Baseline demographic and ocular biometric parameters were collected, including age, eye laterality, cycloplegic refraction, corneal curvature(K1 and K2)and AL. AL change(ΔAL)at 6 mo after lens wearing was defined as the primary outcome. Four machine learning models—decision tree, random forest, extreme gradient boosting(XGBoost), and neural network—were developed to predict treatment efficacy, and their predictive performance was compared. Multiple linear regression analysis was performed to identify relevant factors associated with ΔAL.RESULTS: A total of 102 myopic patients(159 eyes)aged 8-17 y were included in this study, including 57 bilateral eyes and 45 unilateral cases. Among them, 56 were boys(54.9%)and 46 were girls(45.1%). The mean age was 11.98±1.93 y. At baseline, the mean spherical error was -2.28±1.37 D, the mean cylindrical power was -0.65±0.36 D, and AL was 24.60±0.82 mm, the mean flat keratometry(K1)was 42.55±1.39 D, and the mean steep keratometry(K2)was 43.58±1.44 D.After 6 mo of lens wear, the AL increased to 24.68±0.59 mm, The mean spherical power was -2.45±1.46 D, the cylindrical power was -0.68±0.38 D, the K1 value was 42.52±1.33 D, and the K2 value was 43.61±1.51 D. The mean ΔAL was 0.08±0.17 mm.The performance comparison of four machine learning models in predicting the efficacy of OK on AL control showed that the decision tree model achieved the best predictive performance(MAE=0.11, MSE=0.02, R2=0.44). Feature importance analysis indicated that age, baseline AL and K1 were the top three predictors. Multiple linear regression showed that age was negatively associated with ΔAL(β=-0.03, P<0.001), while other variables were not significantly associated(all P>0.05).CONCLUSION: Machine learning models can effectively predict the efficacy of OK in controlling axial elongation in children. The decision tree model shows the best performance. Age is a key factor affecting the therapeutic effect of OK, while baseline AL and corneal curvature also contribute to prediction.This study provides new data support for individualized myopia prevention and control strategies in the pediatric population.
