ArticleFrontiers in medicine2022
Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Genome-wide association study for refractive error imputed from fundus images via deep learning: a proof-of-concept study.Japanese journal of ophthalmology · 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
- A Multimodal Multitask Artificial Intelligence Model for Orthokeratology Contact Lens Fitting: An Integrated Framework to Enhance Lens Centration and Myopia Control Effect.Ophthalmology science · 2026Article
- Artificial Intelligence-Based Prediction of Subjective Refraction and Clinical Determinants of Prediction Error.Diagnostics (Basel, Switzerland) · 2026Article
- A refractive error prediction model for children and adolescents based on ocular biometric parameters.BMC ophthalmology · 2026Observational
- Assessing the reliability of non-cycloplegic refraction in children: a machine learning approach based on non-cycloplegic parameters.Frontiers in public health · 2026Article
- Artificial intelligence for myopia: Current update and concern.Indian journal of ophthalmology · 2026Article
- Prediction of refractive error in adolescents using a multimodal large language model.Frontiers in medicine · 2026Article
- Artificial intelligence applications in refractive error management: A systematic review and meta-analysis.PLOS digital health · 2025Article
- Improving the estimation of the spherical equivalent subjective refraction using objective information on accommodation.Biomedical optics express · 2025Article
- Fundus Refraction Offset as an Individualized Myopia Biomarker.JAMA ophthalmology · 2025Article
- Bibliometric analysis of research on the application of deep learning to ophthalmology.Quantitative imaging in medicine and surgery · 2025Article
- Comparison of B-Scan ultrasonography, ultra-widefield fundus imaging, and indirect ophthalmoscopy in detecting retinal breaks in cataractous eyes.Eye (London, England) · 2024Article
- Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications.Ophthalmology and therapy · 2023Review
- Identification of ocular refraction based on deep learning algorithm as a novel retinoscopy method.Biomedical engineering online · 2022Article
- Ocular image-based deep learning for predicting refractive error: A systematic review.Advances in ophthalmology practice and researchReview
- Deep Imbalanced Regression Model for Predicting Refractive Error from Retinal Photos.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Summary: Ultrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power. Purpose: To explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images. Methods: UWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the "true value" measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient ( Results: ResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with Conclusions: It was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved.
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