ArticleFrontiers in medicine2025
Prediction of myopia onset and shift in premyopic school-aged children: a machine learning-based algorithm.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- EGS-Net: a knowledge-augmented machine learning framework for predicting future high-myopia risk from longitudinal school-screening trajectories.Frontiers in medicine · 2026Article
- Myo-ODE: continuous-time trajectory reconstruction and risk prediction of high myopia via neural ordinary differential equations.Frontiers in public health · 2026Article
- Identifying and Managing Pre-Myopia: A Narrative Review.Clinical ophthalmology (Auckland, N.Z.) · 2026Review
- SSR-stacking: a hybrid GNN-ensemble framework for high-myopia classification under simulated baseline-SE missingness.Frontiers in public health · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study aimed to investigate longitudinal changes in ocular parameters and develop a machine learning-based model for predicting myopia onset and shift within 1 year in school-aged premyopic children. Methods: This prospective cohort study enrolled 320 premyopic children aged 6-12 years from the Ophthalmology Clinic of The Second Affiliated Hospital of Dalian Medical University. Uncorrected visual acuity (logMAR), cycloplegic spherical equivalent (SE), axial length (AL), average corneal curvature (CC), and subfoveal choroidal thickness (SFCT) were measured at baseline and 6-month intervals for 12 months. Premyopia was defined as - 0.50 D < SE ≤ + 0.75 D. A multivariable analysis evaluated predictive factors including age, gender, parental myopia, baseline SE, AL, CC, axial length/corneal radius (AL/CR), and SFCT. Machine learning algorithms were used to predict 1-year myopia onset and myopia shift, along with Shapley Additive exPlanations (SHAP) interpretation. Results: Among 284 participants (88.8% retention rate), 141 children (49.3%) developed myopia. The cohort exhibited an annual SE progression of -0.695 ± 0.222 D and AL elongation of 0.356 ± 0.122 mm. The AL/CR increased from 2.986 ± 0.061 to 3.029 ± 0.072 ( Conclusion: Premyopic children exhibited accelerated myopic progression. Our machine learning-based predictive models showed promising performance for myopia onset and myopia shift, providing clinically valuable risk stratification for targeted prevention strategies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.