ArticleEye (London, England)2022
Deep learning for predicting uncorrected refractive error using posterior segment optical coherence tomography images.
Article in Eye (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Artificial intelligence in pathologic myopia: a review of clinical research studies.Frontiers in medicine · 2025Pooled it
- Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature.Frontiers in medicine · 2025Pooled it
- 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
- Application of artificial intelligence in myopia prevention and control.Pediatric investigation · 2025Review
- Artificial Intelligence in Optometry: Current and Future Perspectives.Clinical optometry · 2025Review
- Global trends and hotspots in artificial intelligence for high myopia: a bibliometric analysis.Frontiers in medicine · 2025Article
- Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era.Medical & biological engineering & computing · 2024Article
- Deep Transfer Learning for Ethnically Distinct Populations: Prediction of Refractive Error Using Optical Coherence Tomography.Ophthalmology and therapy · 2024Article
- Insights into artificial intelligence in myopia management: from a data perspective.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Review
- 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
- Deep learning-based estimation of axial length using macular optical coherence tomography images.Frontiers in medicine · 2023Article
- Artificial intelligence technology for myopia challenges: A review.Frontiers in cell and developmental biology · 2023Review
- Prediction of postoperative visual acuity in patients with age-related cataracts using macular optical coherence tomography-based deep learning method.Frontiers in medicine · 2023Article
- Deep learning for predicting refractive error from multiple photorefraction images.Biomedical engineering online · 2022Article
- Advances in OCT Imaging in Myopia and Pathologic Myopia.Diagnostics (Basel, Switzerland) · 2022Review
- Deep Learning Model Based on 3D Optical Coherence Tomography Images for the Automated Detection of Pathologic Myopia.Diagnostics (Basel, Switzerland) · 2022Article
- Role of artificial intelligence in optical coherence tomography in myopia and pathological myopia.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesThis study aimed to evaluate a deep learning model for estimating uncorrected refractive error using posterior segment optical coherence tomography (OCT) images.
methodsIn this retrospective study, we assigned healthy subjects to development (N = 688 eyes of 344 subjects) and test (N = 248 eyes of 124 subjects) datasets (prospective validation design). We developed and validated OCT-based deep learning models to estimate refractive error. A regression model based on a pretrained ResNet50 architecture was trained using horizontal OCT images to predict the spherical equivalent (SE). The performance of the deep learning model for detecting high myopia was also evaluated. A saliency map was generated using the Grad-CAM technique to visualize the characteristic features.
resultsThe developed model showed a low mean absolute error for SE prediction (2.66 D) and a significant Pearson correlation coefficient of 0.588 (P < 0.001) in the test dataset validation. To detect high myopia, the model yielded an area under the receiver operating characteristic curve of 0.813 (95% confidence interval [CI], 0.744-0.881) and an accuracy of 71.4% (95% CI, 65.3-76.9%). The inner retinal layers and relatively steepened curvatures were highlighted using a saliency map to detect high myopia.
conclusionA deep learning algorithm showed that OCT could potentially be used as an imaging modality to estimate refractive error. This method will facilitate the evaluation of refractive error to prevent clinicians from overlooking the risks associated with refractive error during OCT assessment.
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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.