Prediction of spherical equivalent difference before and after cycloplegia in school-age children with machine learning algorithms

PURPOSE: To predict the need for cycloplegic assessment, as well as refractive state under cycloplegia, based on non-cycloplegic ocular parameters in school-age children. DESIGN: Random cluster sampling. METHODS: The cross-sectional study was conducted from December 2018 to January 2019. Random cluster sampling was used to select 2,467 students aged 6–18 years. All participants were from primary school, middle school and high school. Visual acuity, optical biometry, intraocular pressure, accommodation lag, gaze deviation in primary position, non-cycloplegic and cycloplegic autorefraction were conducted. A binary classification model and a three-way classification model were established to predict the necessity of cycloplegia and the refractive status, respectively. A regression model was also developed to predict the refractive error using machine learning algorithms. RESULTS: The accuracy of the model recognizing requirement of cycloplegia was 68.5–77.0% and the AUC was 0.762–0.833. The model for prediction of SE had performances of R^2 0.889–0.927, MSE 0.250–0.380, MAE 0.372–0.436 and r 0.943–0.963. As the prediction of refractive error status, the accuracy and F1 score was 80.3–81.7% and 0.757–0.775, respectively. There was no statistical difference between the distribution of refractive status predicted by the machine learning models and the one obtained under cycloplegic conditions in school-age students. CONCLUSION: Based on big data acquisition and machine learning techniques, the difference before and after cycloplegia can be effectively predicted in school-age children. This study provides a theoretical basis and supporting evidence for the epidemiological study of myopia and the accurate analysis of vision screening data and optometry services.