ArticleDiagnostics (Basel, Switzerland)2022
Deep Learning Model Based on 3D Optical Coherence Tomography Images for the Automated Detection of Pathologic Myopia.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
What it found
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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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Machine Learning Approaches in High Myopia: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Performances of artificial intelligence in detecting pathologic myopia: a systematic review and meta-analysis.Eye (London, England) · 2023Pooled 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
- Identification of multiple ocular diseases using a hybrid quantum convolutional neural network with fundus images.Scientific reports · 2026Article
- A robust deep learning classifier for screening multiple retinal diseases on optical coherence tomography.Scientific reports · 2025Article
- Artificial intelligence applications in refractive error management: A systematic review and meta-analysis.PLOS digital health · 2025Article
- Deep Learning Model for Automated Classification of Macular Neovascularization Subtypes in AMD.Investigative ophthalmology & visual science · 2025Article
- IMI-Instrumentation for Myopia Management.Investigative ophthalmology & visual science · 2025Review
- Thermal imaging and deep learning-based fit-checking for respiratory protection.Scientific reports · 2024Article
- FacialNet: facial emotion recognition for mental health analysis using UNet segmentation with transfer learning model.Frontiers in computational neuroscience · 2024Article
- Article
- Deviated Saccadic Trajectory as a Biometric Signature of Glaucoma.Translational vision science & technology · 2023Article
- Artificial intelligence technology for myopia challenges: A review.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-based estimation of axial length using macular optical coherence tomography images.Frontiers in medicine · 2023Article
- Special Issue: "Machine Learning for Computer-Aided Diagnosis in Biomedical Imaging".Diagnostics (Basel, Switzerland) · 2022Article
- Novel Uses and Challenges of Artificial Intelligence in Diagnosing and Managing Eyes with High Myopia and Pathologic Myopia.Diagnostics (Basel, Switzerland) · 2022Review
- Global trends and frontiers of research on pathologic myopia since the millennium: A bibliometric analysis.Frontiers in public health · 2022Article
- Applications of Artificial Intelligence in Choroid Visualization for Myopia: A Comprehensive Scoping Review.Middle East African journal of ophthalmologyArticle
- 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
5 authors at 3 institutions in 1 country.
Funding
Abstract
Pathologic myopia causes vision impairment and blindness, and therefore, necessitates a prompt diagnosis. However, there is no standardized definition of pathologic myopia, and its interpretation by 3D optical coherence tomography images is subjective, requiring considerable time and money. Therefore, there is a need for a diagnostic tool that can automatically and quickly diagnose pathologic myopia in patients. This study aimed to develop an algorithm that uses 3D optical coherence tomography volumetric images (C-scan) to automatically diagnose patients with pathologic myopia. The study was conducted using 367 eyes of patients who underwent optical coherence tomography tests at the Ophthalmology Department of Incheon St. Mary's Hospital and Seoul St. Mary's Hospital from January 2012 to May 2020. To automatically diagnose pathologic myopia, a deep learning model was developed using 3D optical coherence tomography images. The model was developed using transfer learning based on four pre-trained convolutional neural networks (ResNet18, ResNext50, EfficientNetB0, EfficientNetB4). Grad-CAM was used to visualize features affecting the detection of pathologic myopia. The performance of each model was evaluated and compared based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The model based on EfficientNetB4 showed the best performance (95% accuracy, 93% sensitivity, 96% specificity, and 98% AUROC) in identifying pathologic myopia.
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Identifiers
What OpenQuestion holds
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.