Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China.
School of Medical Technology, Tianjin Medical University, Tianjin, China.
Front Public Health. 2023 Apr 11;11:1096330. doi: 10.3389/fpubh.2023.1096330. eCollection 2023.
To predict the need for cycloplegic assessment, as well as refractive state under cycloplegia, based on non-cycloplegic ocular parameters in school-age children.
Random cluster sampling.
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.
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.
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.
基于儿童非睫状肌眼部参数，预测睫状肌麻痹评估的必要性以及睫状肌麻痹下的屈光状态。
随机聚类抽样。
本横断面研究于 2018 年 12 月至 2019 年 1 月进行。采用随机聚类抽样方法，选取 2467 名 6-18 岁的在校学生。所有参与者均来自小学、中学和高中。进行视力、光学生物测量、眼压、调节滞后、主位眼位偏斜、非睫状肌和睫状肌自动验光。建立二分类模型和三分类模型分别预测睫状肌麻痹的必要性和屈光状态。还开发了一个回归模型，使用机器学习算法预测屈光误差。
模型识别睫状肌麻痹需求的准确率为 68.5%-77.0%，AUC 为 0.762-0.833。SE 预测模型的 R^2 为 0.889-0.927，MSE 为 0.250-0.380，MAE 为 0.372-0.436，r 为 0.943-0.963。作为屈光误差状态的预测，准确率和 F1 分数分别为 80.3%-81.7%和 0.757-0.775。基于机器学习模型预测的屈光状态分布与睫状肌麻痹条件下获得的屈光状态分布之间无统计学差异。
基于大数据采集和机器学习技术，可以有效预测学龄儿童睫状肌麻痹前后的差异。本研究为近视的流行病学研究以及视力筛查数据和验光服务的准确分析提供了理论依据和支持证据。
