ArticleFrontiers in public health2023
Prediction of spherical equivalent difference before and after cycloplegia in school-age children with machine learning algorithms.
Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Refractive Error Centile Curves for Asian and Western Children and Adolescents: An Individual Level Meta-Analysis.Investigative ophthalmology & visual science · 2025Pooled it
- Machine Learning-Based Prediction of Cycloplegic Refraction Using the Eyerobo Vision Screener: Design-A Cross-Sectional Comparative Device Study.Ophthalmology and therapy · 2026Article
- Development of a deep learning model to predict cycloplegic myopia and pre- myopia: based on a large-scale screening of school-aged children.Eye (London, England) · 2026Article
- The application of artificial intelligence in myopia prediction for children and adolescents: a review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Artificial intelligence in pediatric myopia - a narrative review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Binocular Non-Cycloplegic Ocular Parameters for Modeling Cycloplegic SER in Children.Translational vision science & technology · 2026Article
- Machine learning classification of cycloplegic refractive status in children and adolescents using non-cycloplegic data across ten provincial-level administrative divisions.BMC medical informatics and decision making · 2026Article
- Clinical Validation of Apollo™, A Novel Web-Based Tool for Refractive Error Self-Assessment.Journal of medical systems · 2026Article
- Predicting hyperopic reserve in children based on school-based vision programs with ensemble learning.Frontiers in public health · 2026Article
- Assessing the reliability of non-cycloplegic refraction in children: a machine learning approach based on non-cycloplegic parameters.Frontiers in public health · 2026Article
- Optimizing myopia screening referral guidelines for children aged 4 to 18 based on non-cycloplegic indicators.BMC ophthalmology · 2025Article
- Myopia Prediction Using Machine Learning: An External Validation Study.Vision (Basel, Switzerland) · 2025Article
- Predictive Modeling of Cycloplegic Refraction Using Non-Cycloplegia Ocular Parameters With Emphasis on Lens-Related Features.Translational vision science & technology · 2025Article
- School-level prediction and management of myopia in children and adolescents.Journal of translational medicine · 2025Article
- Influence of dynamic changes of ocular biometric parameters on new-onset myopia in Chinese children: a 4-year cohort study.Scientific reports · 2025Article
- Interpretable machine learning models for predicting childhood myopia from school-based screening data.Scientific reports · 2025Article
- Refractive development III: Variations in emmetropia and ametropia.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025Article
- Machine learning-driven prediction of cycloplegic refractive error in Chinese children.Frontiers in cell and developmental biology · 2025Article
- Correlation analysis of myopia and dietary factors among primary and secondary school students in Shenyang, China.Scientific reports · 2024Article
- Refractive Errors, Amplitude of Accommodation, and Myopia Progression in Kazakhstani Medical Students: 5-Year Follow-Up.Journal of clinical medicine · 2024Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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