ArticleScientific reports2021
Deep learning models for screening of high myopia using optical coherence tomography.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Shaping the future of myopia: artificial intelligence for vitreoretinal complications of high and pathologic myopia.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Interpretable machine learning model based on blood parameters for screening high myopia.Eye (London, England) · 2026Article
- Multi-OCT-SelfNet: integrating self-supervised learning with multi-source data fusion for enhanced multi-class retinal disease classification.Frontiers in systems biology · 2026Article
- Artificial intelligence applications in refractive error management: A systematic review and meta-analysis.PLOS digital health · 2025Article
- Global trends and hotspots in artificial intelligence for high myopia: a bibliometric analysis.Frontiers in medicine · 2025Article
- Artificial Intelligence in Optometry: Current and Future Perspectives.Clinical optometry · 2025Review
- 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
- Deep learning-based stress detection for daily life use using single-channel EEG and GSR in a virtual reality interview paradigm.PloS one · 2024Article
- Initiation of China Alliance of Research in High Myopia (CHARM): protocol for an AI-based multimodal high myopia research biobank.BMJ open · 2023Article
- OCT-based deep-learning models for the identification of retinal key signs.Scientific reports · 2023Article
- Article
- Generative adversarial network-based deep learning approach in classification of retinal conditions with optical coherence tomography images.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Article
- Artificial intelligence-aided diagnosis and treatment in the field of optometry.International journal of ophthalmology · 2023Article
- Artificial intelligence technology for myopia challenges: A review.Frontiers in cell and developmental biology · 2023Review
- 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
- Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening.Frontiers in cell and developmental biology · 2022Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
This study aimed to validate and evaluate deep learning (DL) models for screening of high myopia using spectral-domain optical coherence tomography (OCT). This retrospective cross-sectional study included 690 eyes in 492 patients with OCT images and axial length measurement. Eyes were divided into three groups based on axial length: a "normal group," a "high myopia group," and an "other retinal disease" group. The researchers trained and validated three DL models to classify the three groups based on horizontal and vertical OCT images of the 600 eyes. For evaluation, OCT images of 90 eyes were used. Diagnostic agreements of human doctors and DL models were analyzed. The area under the receiver operating characteristic curve of the three DL models was evaluated. Absolute agreement of retina specialists was 99.11% (range: 97.78-100%). Absolute agreement of the DL models with multiple-column model was 100.0% (ResNet 50), 90.0% (Inception V3), and 72.22% (VGG 16). Areas under the receiver operating characteristic curves of the DL models with multiple-column model were 0.99 (ResNet 50), 0.97 (Inception V3), and 0.86 (VGG 16). The DL model based on ResNet 50 showed comparable diagnostic performance with retinal specialists. The DL model using OCT images demonstrated reliable diagnostic performance to identify high myopia.
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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.