ArticleTranslational vision science & technology2023
Deep Learning-Based System for Disease Screening and Pathologic Region Detection From Optical Coherence Tomography Images.
Article in Translational vision science & technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- TPA-ConvNeXt: Trigonometric Phase Attention for Robust Retinal Disease Classification Across Fundus and OCT.Bioengineering (Basel, Switzerland) · 2026Article
- Article
- 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
- Research on deep learning-based lesion identification in optical coherence tomography.BMC ophthalmology · 2026Article
- Artificial intelligence for precision therapeutics in age-related macular degeneration: current advances, challenges, and future directions.Frontiers in artificial intelligence · 2026Review
- PDC-DETR: a Parallel Dilated Convolutional Detection Transformer for preliminary screening of multiple macular degeneration.BMC medical imaging · 2025Article
- Artificial intelligence-driven diagnosis for age-related macular degeneration bridging pathology and engineering: a survey.International ophthalmology · 2025Review
- A Lightweight CNN for Multiclass Retinal Disease Screening with Explainable AI.Journal of imaging · 2025Article
- Deep Learning Model for Automated Classification of Macular Neovascularization Subtypes in AMD.Investigative ophthalmology & visual science · 2025Article
- Residual self-attention vision transformer for detecting acquired vitelliform lesions and age-related macular drusen.Scientific reports · 2025Article
- A Comprehensive CNN Model for Age-Related Macular Degeneration Classification Using OCT: Integrating Inception Modules, SE Blocks, and ConvMixer.Diagnostics (Basel, Switzerland) · 2024Article
- A new intelligent system based deep learning to detect DME and AMD in OCT images.International ophthalmology · 2024Article
- Inter-rater reliability in labeling quality and pathological features of retinal OCT scans: A customized annotation software approach.PloS one · 2024Article
- The Role of Artificial Intelligence in Epiretinal Membrane Care: A Scoping Review.Ophthalmology scienceReview
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study was designed to apply deep learning models in retinal disease screening and lesion detection based on optical coherence tomography (OCT) images. Methods: We collected 37,138 OCT images from 775 patients and labelled by ophthalmologists. Multiple deep learning models including ResNet50 and YOLOv3 were developed to identify the types and locations of diseases or lesions based on the images. Results: The model were evaluated using patient-based independent holdout set. For binary classification of OCT images with or without lesions, the performance accuracy was 98.5%, sensitivity was 98.7%, specificity was 98.4%, and the F1 score was 97.7%. For multiclass multilabel disease classification, the models was able to detect vitreomacular traction syndrome and age-related macular degeneration both with an accuracy of more than 99%, sensitivity of more than 98%, specificity of more than 98%, and an F1 score of more than 97%. For lesion location detection, the recalls for different lesion types ranged from 87.0% (epiretinal membrane) to 98.2% (macular pucker). Conclusions: Deep learning-based models have potentials to aid retinal disease screening, classification and diagnosis with excellent performance, which may serve as useful references for ophthalmologists. Translational Relevance: The deep learning-based models are capable of identifying and predicting different eye diseases and lesions from OCT images and may have potential clinical application to assist the ophthalmologists for fast and accuracy retinal disease screening.
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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.