ArticleScientific reports2020
Epiretinal Membrane Detection at the Ophthalmologist Level using Deep Learning of Optical Coherence Tomography.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 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
- DenseViT-OCT: A Hybrid CNN-Transformer Architecture with Multi-Scale Dense Feature Aggregation for Automated Epiretinal Membrane Severity Classification.Tomography (Ann Arbor, Mich.) · 2026Article
- PDC-DETR: a Parallel Dilated Convolutional Detection Transformer for preliminary screening of multiple macular degeneration.BMC medical imaging · 2025Article
- Optical Coherence Tomography Characteristics Between Idiopathic Epiretinal Membranes and Secondary Epiretinal Membranes due to Peripheral Retinal Hole.Journal of ophthalmology · 2025Article
- Low-Cost and Fast Epiretinal Membrane Detection and Quantification Based on SD-OCT.IEEE access : practical innovations, open solutions · 2025Article
- Interpretable detection of epiretinal membrane from optical coherence tomography with deep neural networks.Scientific reports · 2024Article
- Clinical evaluation of deep learning systems for assisting in the diagnosis of the epiretinal membrane grade in general ophthalmologists.Eye (London, England) · 2024Article
- Transforming the future of ophthalmology: artificial intelligence and robotics' breakthrough role in surgical and medical retina advances: a mini review.Frontiers in medicine · 2024Review
- Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images.International journal of ophthalmology · 2024Review
- OCT-based deep-learning models for the identification of retinal key signs.Scientific reports · 2023Article
- Correlation between Severity of Idiopathic Epiretinal Membrane and Irvine-Gass Syndrome.Journal of personalized medicine · 2023Article
- The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use.Journal of Korean medical science · 2023Review
- Screening of idiopathic epiretinal membrane using fundus images combined with blood oxygen saturation and vascular morphological features.International ophthalmology · 2023Article
- iERM: An Interpretable Deep Learning System to Classify Epiretinal Membrane for Different Optical Coherence Tomography Devices: A Multi-Center Analysis.Journal of clinical medicine · 2023Article
- Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach.Sensors (Basel, Switzerland) · 2022Article
- Deep Learning Models for Segmenting Non-perfusion Area of Color Fundus Photographs in Patients With Branch Retinal Vein Occlusion.Frontiers in medicine · 2022Article
- Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening.Frontiers in cell and developmental biology · 2022Article
- Short-TermBioMed research international · 2022Article
- Artificial intelligence for improving sickle cell retinopathy diagnosis and management.Eye (London, England) · 2021Review
- 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
purposePrevious deep learning studies on optical coherence tomography (OCT) mainly focused on diabetic retinopathy and age-related macular degeneration. We proposed a deep learning model that can identify epiretinal membrane (ERM) in OCT with ophthalmologist-level performance.
designCross-sectional study.
participantsA total of 3,618 central fovea cross section OCT images from 1,475 eyes of 964 patients.
methodsWe retrospectively collected 7,652 OCT images from 1,197 patients. From these images, 2,171 were normal and 1,447 were ERM OCT. A total of 3,141 OCT images was used as training dataset and 477 images as testing dataset. DL algorithm was used to train the interpretation model. Diagnostic results by four board-certified non-retinal specialized ophthalmologists on the testing dataset were compared with those generated by the DL model.
main outcome measuresWe calculated for the derived DL model the following characteristics: sensitivity, specificity, F1 score and area under curve (AUC) of the receiver operating characteristic (ROC) curve. These were calculated according to the gold standard results which were parallel diagnoses of the retinal specialist. Performance of the DL model was finally compared with that of non-retinal specialized ophthalmologists.
resultsRegarding the diagnosis of ERM in OCT images, the trained DL model had the following characteristics in performance: sensitivity: 98.7%, specificity: 98.0%, and F1 score: 0.945. The accuracy on the training dataset was 99.7% (95% CI: 99.4 - 99.9%), and for the testing dataset, diagnostic accuracy was 98.1% (95% CI: 96.5 - 99.1%). AUC of the ROC curve was 0.999. The DL model slightly outperformed the average non-retinal specialized ophthalmologists.
conclusionsAn ophthalmologist-level DL model was built here to accurately identify ERM in OCT images. The performance of the model was slightly better than the average non-retinal specialized ophthalmologists. The derived model may play a role to assist clinicians to promote the efficiency and safety of healthcare in the future.
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