ArticlePloS one2020
Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 5 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 5 syntheses or guidelines pooled it, 69 citations in OpenAlex.
- [Optimization of Comprehensive Prevention and Control Strategies for Myopia From the Persepctive of the Social Ecological Systems Theory].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2025Pooled it
- Machine Learning Approaches in High Myopia: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature.Frontiers in medicine · 2025Pooled it
- Artificial intelligence in pathologic myopia: a review of clinical research studies.Frontiers in medicine · 2025Pooled it
- Performances of artificial intelligence in detecting pathologic myopia: a systematic review and meta-analysis.Eye (London, England) · 2023Pooled it
- Advancements in AI-based quantitative analysis of fundus tessellation and its application in myopia research.Frontiers in medicine · 2026Review
- IMI-Instrumentation for Myopia Management.Investigative ophthalmology & visual science · 2025Review
- Development of Deep Learning Models to Screen Posterior Staphylomas in Highly Myopic Eyes Using UWF-OCT Images.Translational vision science & technology · 2025Article
- Diagnostic challenges in high myopia: identification of sight-threatening complications and the role of artificial intelligence.Frontiers in ophthalmology · 2025Review
- 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
- Improve the efficiency and accuracy of ophthalmologists' clinical decision-making based on AI technology.BMC medical informatics and decision making · 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
- Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm.International journal of ophthalmology · 2023Article
- Artificial intelligence technology for myopia challenges: A review.Frontiers in cell and developmental biology · 2023Review
- Advances in artificial intelligence models and algorithms in the field of optometry.Frontiers in cell and developmental biology · 2023Review
- Artificial intelligence-aided diagnosis and treatment in the field of optometry.International journal of ophthalmology · 2023Article
- Deep learning for predicting uncorrected refractive error using posterior segment optical coherence tomography images.Eye (London, England) · 2022Article
- Advances in OCT Imaging in Myopia and Pathologic Myopia.Diagnostics (Basel, Switzerland) · 2022Review
- An Artificial-Intelligence-Based Automated Grading and Lesions Segmentation System for Myopic Maculopathy Based on Color Fundus Photographs.Translational vision science & technology · 2022Article
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
This study examined and compared outcomes of deep learning (DL) in identifying swept-source optical coherence tomography (OCT) images without myopic macular lesions [i.e., no high myopia (nHM) vs. high myopia (HM)], and OCT images with myopic macular lesions [e.g., myopic choroidal neovascularization (mCNV) and retinoschisis (RS)]. A total of 910 SS-OCT images were included in the study as follows and analyzed by k-fold cross-validation (k = 5) using DL's renowned model, Visual Geometry Group-16: nHM, 146 images; HM, 531 images; mCNV, 122 images; and RS, 111 images (n = 910). The binary classification of OCT images with or without myopic macular lesions; the binary classification of HM images and images with myopic macular lesions (i.e., mCNV and RS images); and the ternary classification of HM, mCNV, and RS images were examined. Additionally, sensitivity, specificity, and the area under the curve (AUC) for the binary classifications as well as the correct answer rate for ternary classification were examined. The classification results of OCT images with or without myopic macular lesions were as follows: AUC, 0.970; sensitivity, 90.6%; specificity, 94.2%. The classification results of HM images and images with myopic macular lesions were as follows: AUC, 1.000; sensitivity, 100.0%; specificity, 100.0%. The correct answer rate in the ternary classification of HM images, mCNV images, and RS images were as follows: HM images, 96.5%; mCNV images, 77.9%; and RS, 67.6% with mean, 88.9%.Using noninvasive, easy-to-obtain swept-source OCT images, the DL model was able to classify OCT images without myopic macular lesions and OCT images with myopic macular lesions such as mCNV and RS with high accuracy. The study results suggest the possibility of conducting highly accurate screening of ocular diseases using artificial intelligence, which may improve the prevention of blindness and reduce workloads for ophthalmologists.
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