ArticlePrecision clinical medicine2024
Accurate prediction of myopic progression and high myopia by machine learning.
Article in Precision clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- 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
- Artificial intelligence in pathologic myopia: a review of clinical research studies.Frontiers in medicine · 2025Pooled it
- 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
- Binocular Non-Cycloplegic Ocular Parameters for Modeling Cycloplegic SER in Children.Translational vision science & technology · 2026Article
- Review
- Ocular biometric profile and clinical characteristics of high myopia in Indian children.Indian journal of ophthalmology · 2026Article
- Artificial Intelligence-Based Prediction of Subjective Refraction and Clinical Determinants of Prediction Error.Diagnostics (Basel, Switzerland) · 2026Article
- AI-guided personalized predictions on myopia progression and interventions.NPJ digital medicine · 2026Article
- Myo-ODE: continuous-time trajectory reconstruction and risk prediction of high myopia via neural ordinary differential equations.Frontiers in public health · 2026Article
- SSR-stacking: a hybrid GNN-ensemble framework for high-myopia classification under simulated baseline-SE missingness.Frontiers in public health · 2026Article
- Navigating the Core Challenges of Atropine Therapy in Myopia: A Mechanistic and Clinical Analysis of Dose, Response, and Durability.Journal of ophthalmology · 2026Review
- EGS-Net: a knowledge-augmented machine learning framework for predicting future high-myopia risk from longitudinal school-screening trajectories.Frontiers in medicine · 2026Article
- Machine learning identifies pupil size and corneal thickness as key predictors of axial elongation rate.PloS one · 2026Article
- Artificial intelligence applications in refractive error management: A systematic review and meta-analysis.PLOS digital health · 2025Article
- Correlation Between Relative Peripheral Refraction and Efficacy of Myopia Control After Wearing Multifocal Soft Contact Lenses in Children.Ophthalmology and therapy · 2025Article
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
- Shaping the future of myopia with artificial intelligence: Mapping trends and promising directions.Advances in ophthalmology practice and researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Myopia is a leading cause of visual impairment in Asia and worldwide. However, accurately predicting the progression of myopia and the high risk of myopia remains a challenge. This study aims to develop a predictive model for the development of myopia. Methods: We first retrospectively gathered 612 530 medical records from five independent cohorts, encompassing 227 543 patients ranging from infants to young adults. Subsequently, we developed a multivariate linear regression algorithm model to predict the progression of myopia and the risk of high myopia. Result: The model to predict the progression of myopia achieved an R Conclusion: Our study demonstrates accurate prediction of myopia progression and risk of high myopia providing valuable insights for tailoring strategies to personalize and optimize the clinical management of myopia in children.
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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 OpenQuestion graph.