ArticleBiomedical engineering online2022
Deep learning for predicting refractive error from multiple photorefraction images.
Article in Biomedical engineering online, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- 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
- Deep learning for predicting myopia severity classification method.Biomedical engineering online · 2025Article
- Artificial Intelligence in Optometry: Current and Future Perspectives.Clinical optometry · 2025Review
- Large-scale study in Chengdu, China: The prevalence of myopia full-correction decreased with increasing myopia in adolescents.Heliyon · 2024Article
- Identification of ocular refraction based on deep learning algorithm as a novel retinoscopy method.Biomedical engineering online · 2022Article
- Deep Imbalanced Regression Model for Predicting Refractive Error from Retinal Photos.Ophthalmology scienceArticle
- Ocular image-based deep learning for predicting refractive error: A systematic review.Advances in ophthalmology practice and researchReview
- Role of artificial intelligence in optical coherence tomography in myopia and pathological myopia.Taiwan journal of ophthalmologyReview
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Authors and funding
7 authors.
Funding
Abstract
backgroundRefractive error detection is a significant factor in preventing the development of myopia. To improve the efficiency and accuracy of refractive error detection, a refractive error detection network (REDNet) is proposed that combines the advantages of a convolutional neural network (CNN) and a recurrent neural network (RNN). It not only extracts the features of each image, but also fully utilizes the sequential relationship between images. In this article, we develop a system to predict the spherical power, cylindrical power, and spherical equivalent in multiple eccentric photorefraction images. Approach First, images of the pupil area are extracted from multiple eccentric photorefraction images; then, the features of each pupil image are extracted using the REDNet convolution layers. Finally, the features are fused by the recurrent layers in REDNet to predict the spherical power, cylindrical power, and spherical equivalent.
resultsThe results show that the mean absolute error (MAE) values of the spherical power, cylindrical power, and spherical equivalent can reach 0.1740 D (diopters), 0.0702 D, and 0.1835 D, respectively. SIGNIFICANCE: This method demonstrates a much higher accuracy than those of current state-of-the-art deep-learning methods. Moreover, it is effective and practical.
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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.