ArticleJMIR medical informatics2020
Deep Learning-Based Prediction of Refractive Error Using Photorefraction Images Captured by a Smartphone: Model Development and Validation Study.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 43 citations in OpenAlex.
- Effectiveness of smartphone technology for detection of paediatric ocular diseases-a systematic review.BMC ophthalmology · 2025Pooled it
- Machine Learning Approaches in High Myopia: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Myopia prediction: a systematic review.Eye (London, England) · 2022Pooled it
- 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
- Myopia Prescription Based on Smartphone App: A Feasibility Study in Africa.Translational vision science & technology · 2026Article
- Association between fundus tessellated density and subfoveal choroidal thickness in children with different refractive statuses.Frontiers in medicine · 2026Article
- Prediction of refractive error in adolescents using a multimodal large language model.Frontiers in medicine · 2026Article
- Accuracy and precision of a sphero-cylindrical over-refraction app for smartphones.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025Article
- Artificial intelligence applications in refractive error management: A systematic review and meta-analysis.PLOS digital health · 2025Article
- Artificial intelligence virtual assistants in primary eye care practice.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025Article
- Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study.International journal of molecular sciences · 2023Article
- Automatic Refractive Error Estimation Using Deep Learning-Based Analysis of Red Reflex Images.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications.Ophthalmology and therapy · 2023Review
- Artificial intelligence and machine learning in ophthalmology: A review.Indian journal of ophthalmology · 2023Review
- Identification of ocular refraction based on deep learning algorithm as a novel retinoscopy method.Biomedical engineering online · 2022Article
- Deep learning for predicting refractive error from multiple photorefraction images.Biomedical engineering online · 2022Article
- COVID-19 myopia, myopia of pandemic: Are we heading towards unpredictable high myopic era?Indian journal of ophthalmology · 2022Article
- Prediction of Subjective Refraction From Anterior Corneal Surface, Eye Lengths, and Age Using Machine Learning Algorithms.Translational vision science & technology · 2022Article
- Artificial Intelligence to Reduce Ocular Health Disparities: Moving From Concept to Implementation.Translational vision science & technology · 2021Article
- Deep Learning-Based Estimation of Axial Length and Subfoveal Choroidal Thickness From Color Fundus Photographs.Frontiers in cell and developmental biology · 2021Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurately predicting refractive error in children is crucial for detecting amblyopia, which can lead to permanent visual impairment, but is potentially curable if detected early. Various tools have been adopted to more easily screen a large number of patients for amblyopia risk.
objectiveFor efficient screening, easy access to screening tools and an accurate prediction algorithm are the most important factors. In this study, we developed an automated deep learning-based system to predict the range of refractive error in children (mean age 4.32 years, SD 1.87 years) using 305 eccentric photorefraction images captured with a smartphone.
methodsPhotorefraction images were divided into seven classes according to their spherical values as measured by cycloplegic refraction.
resultsThe trained deep learning model had an overall accuracy of 81.6%, with the following accuracies for each refractive error class: 80.0% for ≤-5.0 diopters (D), 77.8% for >-5.0 D and ≤-3.0 D, 82.0% for >-3.0 D and ≤-0.5 D, 83.3% for >-0.5 D and <+0.5 D, 82.8% for ≥+0.5 D and <+3.0 D, 79.3% for ≥+3.0 D and <+5.0 D, and 75.0% for ≥+5.0 D. These results indicate that our deep learning-based system performed sufficiently accurately.
conclusionsThis study demonstrated the potential of precise smartphone-based prediction systems for refractive error using deep learning and further yielded a robust collection of pediatric photorefraction images.
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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.