Frontiers in public health · 2023
Prediction of spherical equivalent difference before and after cycloplegia in school-age children with machine learning algorithms.
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Du Bei, Wang Qingxin, Luo Yuan, Jin Nan, Rong Hua, Wang Xilian, Nian Hong, Guo Li, Liang Meng, Wei Ruihua
Аннотация
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.
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.