ArticleOphthalmology and therapy2024
Association Between Myopia and Pupil Diameter in Preschoolers: Evidence from a Machine Learning Approach Based on a Real-World Large-Scale Dataset.
Article in Ophthalmology and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Exploratory multivariate adaptive regression splines modeling of myopic progression in children receiving atropine.Journal of the Chinese Medical Association : JCMA · 2026Article
- 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
- Machine learning identifies pupil size and corneal thickness as key predictors of axial elongation rate.PloS one · 2026Article
- Comparative analysis of refractive errors and ocular biological parameters among pre-myopic adolescents across different age groups and geographical regions.Scientific reports · 2025Article
- Optical and Visual Diet in Myopia.Investigative ophthalmology & visual science · 2025Review
- Development of risk prediction model for small incision lenticule extraction.Frontiers in medicine · 2025Article
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11 authors.
Funding
Abstract
introductionPrevious studies have explored the connections between various ocular biological parameters with myopia. Our previous study also found that pupil data can predict the myopic progression during the interventions for myopia. However, studies exploring the association between pupil diameter and myopia in preschoolers with myopia were lacking. Hence this study was aimed to investigate the association between pupil diameter and myopia in preschoolers with myopia based on a real-world, large-scale dataset.
methodsData containing 650,671 preschoolers were collected from a total of 1943 kindergartens in Shenzhen, China. Refraction and pupil parameters were collected. After data filtering, the occurrence of myopia and its association with age, gender, pupil diameter, and other variables, were analyzed. Random forest (RF) and eXtreme gradient boosting (XGBoost) were selected from seven machine learning algorithms to build the model. The mean decrease accuracy (MDA), mean decrease Gini (MDG), and gain feature importance (GFI) techniques were employed to quantify the importance of pupil diameter and other features.
resultsAfter the assessments, 51,325 valid records with complete pupil data were included, and 3468 (6.76%) were identified as myopia based on the calculated cycloplegic refraction. Preschoolers with myopia presented reduced pupil diameter and greater variation (5.00 ± 0.99 mm) compared to non-myopic preschoolers (6.22 ± 0.67 mm). A nonlinear relationship was found according to the scatterplots between pupil diameter and refraction (R
conclusionsBased on a real-world large-scale dataset, the current study illuminated that preschoolers with myopia had a reduced pupil diameter compared to emmetropic preschoolers with a nonlinear pattern. Machine learning algorithms visualized and validated the pivotal role of pupil diameter in myopia.
trial registrationchictr.org Identifier: ChiCTR2200057391.
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