ArticleFrontiers in medicine2024
Machine-learning models to predict myopia in children and adolescents.
Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Polygenic Risk Scores for Myopia: A Systematic Review of Predictive Performance and Clinical Potential.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Pooled it
- 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
- Reduced myopia progression with low-dose atropine in DIMS non-responders and evidence of altered pupillary light reflex kinetics.Scientific reports · 2026Article
- Binocular Non-Cycloplegic Ocular Parameters for Modeling Cycloplegic SER in Children.Translational vision science & technology · 2026Article
- Aberrant Functional Lateralization and Interhemispheric Synergy in High Myopia: Insights From Neurotransmitter Distribution Signatures and Machine Learning Classification.Investigative ophthalmology & visual science · 2026Article
- Estimation of cycloplegic spherical refraction from non-cycloplegic clinical and biometric data in children using machine learning: a retrospective pilot study for screening triage.BMC ophthalmology · 2026Article
- Article
- Artificial intelligence for myopia: Current update and concern.Indian journal of ophthalmology · 2026Article
- Myo-ODE: continuous-time trajectory reconstruction and risk prediction of high myopia via neural ordinary differential equations.Frontiers in public health · 2026Article
- Machine learning identifies pupil size and corneal thickness as key predictors of axial elongation rate.PloS one · 2026Article
- Predicting myopia risk using a machine learning model based on fundus imageomics.Scientific reports · 2025Article
- Benefits of clean air for school children's vision health.PNAS nexus · 2025Article
- Prediction of myopia onset and shift in premyopic school-aged children: a machine learning-based algorithm.Frontiers in medicine · 2025Article
- Nomogram-based prediction of continued axial elongation in children undergoing orthokeratology.Frontiers in medicine · 2025Article
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: To explore machine-learning applications in myopia prediction and analyze the influencing factors of myopia. Methods: Stratified cluster random sampling was used to select elementary school students in Shenzhen, China for inclusion in this case-control study. Myopia screening, ocular biological parameter measurements, and questionnaires were conducted. Random forest (RF), decision tree (DT), extreme gradient boosting trees (XGBoost), support vector machine (SVM), and logistic regression (LR) algorithms were used to construct five myopia prediction models using R software (version 4.3.0). These myopia prediction models were used to investigate the relationship between ocular biological parameters, environmental factors, behavioral factors, genetic factors, and myopia. Results: This study included 2,947 elementary school students, with a myopia prevalence rate of 47.2%. All five prediction models had an area under the receiver operating characteristic curve (AUC) above 0.75, with prediction accuracy and precision exceeding 0.70. The AUCs in the testing set were 0.846, 0.837, 0.833, and 0.815 for SVM, LR, RF, and XGBoost, respectively, indicating their superior predictive performance to that of DT (0.791). In the RF model, the five most important variables were axial length, age, sex, maternal myopia, and feeding pattern. LR identified axial length was the most significant risk factor for myopia [odds ratio (OR) =8.203], followed by sex (OR = 2.349), maternal myopia (OR = 1.437), Reading and writing posture (OR = 1.270), infant feeding pattern (OR = 1.207), and age (OR = 1.168); corneal radius (OR = 0.034) and anterior chamber depth (OR = 0.516) served as protective factors. Conclusion: Myopia prediction models based on machine learning demonstrated favorable predictive performance and accurately identified myopia risk factors, and may therefore aid in the implementation of myopia prevention and control measures among high-risk individuals.
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