ArticleDiagnostics (Basel, Switzerland)2023
The Classification of Common Macular Diseases Using Deep Learning on Optical Coherence Tomography Images with and without Prior Automated Segmentation.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Detection of cystoid macular edema in patients with retinitis pigmentosa based on deep learning.International journal of retina and vitreous · 2025Article
- Early Detection of Cystoid Macular Edema in Retinitis Pigmentosa Using Longitudinal Deep Learning Analysis of OCT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- MSLI-Net: retinal disease detection network based on multi-segment localization and multi-scale interaction.Frontiers in cell and developmental biology · 2025Article
- Artificial intelligence applications in ophthalmic optical coherence tomography: a 12-year bibliometric analysis.International journal of ophthalmology · 2024Article
- Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images.PloS one · 2024Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We compared the performance of deep learning (DL) in the classification of optical coherence tomography (OCT) images of macular diseases between automated classification alone and in combination with automated segmentation. OCT images were collected from patients with neovascular age-related macular degeneration, polypoidal choroidal vasculopathy, diabetic macular edema, retinal vein occlusion, cystoid macular edema in Irvine-Gass syndrome, and other macular diseases, along with the normal fellow eyes. A total of 14,327 OCT images were used to train DL models. Three experiments were conducted: classification alone (CA), use of automated segmentation of the OCT images by RelayNet, and the graph-cut technique before the classification (combination method 1 (CM1) and 2 (CM2), respectively). For validation of classification of the macular diseases, the sensitivity, specificity, and accuracy of CA were found at 62.55%, 95.16%, and 93.14%, respectively, whereas the sensitivity, specificity, and accuracy of CM1 were found at 72.90%, 96.20%, and 93.92%, respectively, and of CM2 at 71.36%, 96.42%, and 94.80%, respectively. The accuracy of CM2 was statistically higher than that of CA (
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