ArticleNPJ digital medicine2023
Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
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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
44 citing papers in PubMed, 2 syntheses or guidelines pooled it, 66 citations in OpenAlex.
- Machine Learning Approaches in High Myopia: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled 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
- The application of artificial intelligence in myopia prediction for children and adolescents: a review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Trend of Myopia Incidence Among Adolescents (2018-2025) in Senior High School: An 8-Year Retrospective Study in Multi-Regions of China, With Projections to 2030.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Article
- Detection of Myopia from Colour Fundus Photographs Using YOLO-Based Computer-Vision Models: A Comparison with Expert Ophthalmologists.Journal of clinical medicine · 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
- Clinical Performance and Implementation of AI-Enabled Paediatric Ophthalmic Screening, Triage, Diagnosis, and Surveillance in Primary, Community, and Referral-Linked Pathways: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Interpretable machine learning model based on blood parameters for screening high myopia.Eye (London, England) · 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
- Deep Learning Prediction of Childhood Myopia Progression Using Fundus Image and Refraction Data.JAMA network open · 2026Article
- Advancements in AI-based quantitative analysis of fundus tessellation and its application in myopia research.Frontiers in medicine · 2026Review
- Routine baseline clinical data do not reliably predict poor first-year myopia control with low-dose atropine: a machine-learning benchmarking study.Frontiers in ophthalmology · 2026Article
- Benchmark analysis of myopia-related issues using large language models: a comparison of ChatGPT-4o and deepseek.BMC ophthalmology · 2025Article
- Automatic quantification, grading and five-year prediction of myopic fundus tessellation: a multi-center, longitudinal deep learning study.Science China. Life sciences · 2025Article
- Artificial intelligence applications in refractive error management: A systematic review and meta-analysis.PLOS digital health · 2025Article
- Evaluating efficacy of 0.125% atropine using a myopia progression machine learning model.Japanese journal of ophthalmology · 2025Article
- Integrating biometric and multimodal imaging data for early prediction of myopia onset.Scientific reports · 2025Article
- IMI-Instrumentation for Myopia Management.Investigative ophthalmology & visual science · 2025Review
Corrections and comments
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Authors and funding
13 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising of 998 children (aged 6-12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms - image, clinical and mix (image + clinical) models to predict high myopia development (SE ≤ -6.00 diopter) during teenage years (5 years later, age 11-17). Model performance is evaluated using area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93-0.95; Test dataset 0.91-0.93), clinical models (Primary dataset AUC 0.90-0.97; Test dataset 0.93-0.94) and mixed (image + clinical) models (Primary dataset AUC 0.97; Test dataset 0.97-0.98) achieve clinically acceptable performance. The addition of 1 year SE progression variable has minimal impact on the DLS performance (clinical model AUC 0.98 versus 0.97 in primary dataset, 0.97 versus 0.94 in test dataset; mixed model AUC 0.99 versus 0.97 in primary dataset, 0.95 versus 0.98 in test dataset). Thus, our DLS allows prediction of the development of high myopia by teenage years amongst school-going children. This has potential utility as a clinical-decision support tool to identify "at-risk" children for early intervention.
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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.