ArticleTranslational vision science & technology2022
Prediction for Cycloplegic Refractive Error in Chinese School Students: Model Development and Validation.
Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 17 citations in OpenAlex.
- 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
- Assessing the reliability of non-cycloplegic refraction in children: a machine learning approach based on non-cycloplegic parameters.Frontiers in public health · 2026Article
- Rapid and accurate prediction of cycloplegic refraction in Chinese children: development and validation of machine learning models.Journal of global health · 2025Article
- 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
- Predicting Cycloplegic Spherical Equivalent Refraction Among Children and Adolescents Using Non-cycloplegic Data and Machine Learning - China, 2020-2024.China CDC weekly · 2025Article
- Predictive Modeling of Cycloplegic Refraction Using Non-Cycloplegia Ocular Parameters With Emphasis on Lens-Related Features.Translational vision science & technology · 2025Article
- Refractive development III: Variations in emmetropia and ametropia.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025Article
- Short Sleep Duration Is Associated with Overestimated Myopia in University Students.Nature and science of sleep · 2025Article
- Machine learning-driven prediction of cycloplegic refractive error in Chinese children.Frontiers in cell and developmental biology · 2025Article
- Machine Learning Models for Predicting Cycloplegic Refractive Error and Myopia Status Based on Non-Cycloplegic Data in Chinese Students.Translational vision science & technology · 2024Article
- IMI 2023 Digest.Investigative ophthalmology & visual science · 2023Article
- Prediction of spherical equivalent difference before and after cycloplegia in school-age children with machine learning algorithms.Frontiers in public health · 2023Article
- Ocular biometrics and uncorrected visual acuity for detecting myopia in Chinese school students.Scientific reports · 2022Article
- Sex differences in ocular biometric measurements: A twin study.Frontiers in medicine · 2022Article
- Instrument-based, non-cycloplegic versus cycloplegic refraction in pediatric and young adult populations (≤25 years): A systematic review and meta-analysis.Advances in ophthalmology practice and researchReview
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Authors and funding
16 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To predict cycloplegic refractive error using measurements obtained under noncycloplegic conditions. Method: Refractive error was measured in 5- to 18-year-old Chinese students using a NIDEK autorefractor before and after administration of 0.5% tropicamide. Spherical equivalent (SER) in diopters (D) was calculated as sphere plus half cylinder. A multivariable prediction model for cycloplegic SER was developed using data from students in Jinyun (n = 1938) and was validated using data from students in Hangzhou (n = 1498). The performance of the prediction model was evaluated using R2, mean difference between predicted and measured cycloplegic SER, and sensitivity and specificity for predicting myopia (cycloplegic SER ≤ -0.5 D). Results: Among 3436 students (mean age, 9.7 years; 51% female), the mean (SD) noncycloplegic and cycloplegic SER values were -1.12 (1.97) D and -0.20 (2.19) D, respectively. The prediction model that included demographics, noncycloplegic SER, axial length/corneal curvature radius ratio, uncorrected visual acuity (UCVA), and intraocular pressure predicted cycloplegic SER with R2 of 0.93 in the development dataset and 0.92 in the validation dataset. The mean (SD) differences between predicted and measured cycloplegic SER were 0.0 (0.55) D in the development dataset and 0.06 (0.64) D in the validation dataset. In both the development and validation datasets, the combination of predicted SER and UCVA yielded high sensitivity (91.4% and 91.9%, respectively) and specificity (95.0% and 90.1%, respectively) for detecting myopia. Conclusions: Cycloplegic refractive error can be predicted using measurements obtained under noncycloplegic conditions. The prediction model could potentially be used to correct the myopia prevalence in epidemiological studies in which administering cycloplegic agent on all participants is not feasible. Translational Relevance: The prediction model may provide a tool for correcting the overestimation of myopia from noncycloplegic refractive error in future epidemiological studies in which administering cycloplegic agent on all participants is not feasible.
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