ArticleOphthalmology and therapy2019
Optical Coherence Tomography-Based Deep-Learning Models for Classifying Normal and Age-Related Macular Degeneration and Exudative and Non-Exudative Age-Related Macular Degeneration Changes.
Article in Ophthalmology and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 3 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Pooled it
- Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT: Age-Related Eye Disease Study 2: 10-Year Follow-On Study.Ophthalmology · 2021Trial
- Real-world evaluation of an OCT-based AI decision-support system for neovascular AMD activity triage in teleophthalmology.Frontiers in ophthalmology · 2026Article
- Evaluation of deep learning-based retinal pigment epithelium segmentation for a widely used optical coherence tomography device.Scientific reports · 2025Article
- Artificial intelligence-driven diagnosis for age-related macular degeneration bridging pathology and engineering: a survey.International ophthalmology · 2025Review
- A Deep Learning Network for Accurate Retinal Multidisease Diagnosis Using Multiview Fusion of En Face and B-Scan Images: A Multicenter Study.Translational vision science & technology · 2024Article
- Recent advances in the application of artificial intelligence in age-related macular degeneration.BMJ open ophthalmology · 2024Review
- AMD-SD: An Optical Coherence Tomography Image Dataset for wet AMD Lesions Segmentation.Scientific data · 2024Article
- Dry age-related macular degeneration classification from optical coherence tomography images based on ensemble deep learning architecture.Frontiers in medicine · 2024Article
- Predicting Clinician Fixations on Glaucoma OCT Reports via CNN-Based Saliency Prediction Methods.IEEE open journal of engineering in medicine and biology · 2024Article
- Deep learning in optical coherence tomography: Where are the gaps?Clinical & experimental ophthalmology · 2023Review
- Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review.Ophthalmology and therapy · 2023Review
- Robust AMD Stage Grading with Exclusively OCTA Modality Leveraging 3D Volume.... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision · 2023Article
- Generative adversarial network-based deep learning approach in classification of retinal conditions with optical coherence tomography images.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Article
- Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting.PloS one · 2023Article
- Deep learning identify retinal nerve fibre and choroid layers as markers of age-related macular degeneration in the classification of macular spectral-domain optical coherence tomography volumes.Acta ophthalmologica · 2022Article
- Automated artificial intelligence-based system for clinical follow-up of patients with age-related macular degeneration.Acta ophthalmologica · 2022Article
- Use of multimodal dataset in AI for detecting glaucoma based on fundus photographs assessed with OCT: focus group study on high prevalence of myopia.BMC medical imaging · 2022Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe use of optical coherence tomography (OCT) images is increasing in the medical treatment of age-related macular degeneration (AMD), and thus, the amount of data requiring analysis is increasing. Advances in machine-learning techniques may facilitate processing of large amounts of medical image data. Among deep-learning methods, convolution neural networks (CNNs) show superior image recognition ability. This study aimed to build deep-learning models that could distinguish AMD from healthy OCT scans and to distinguish AMD with and without exudative changes without using a segmentation algorithm.
methodsThis was a cross-sectional observational clinical study. A total of 1621 spectral domain (SD)-OCT images of patients with AMD and a healthy control group were studied. The first CNN model was trained and validated using 1382 AMD images and 239 normal images. The second transfer-learning model was trained and validated with 721 AMD images with exudative changes and 661 AMD images without any exudate. The attention area of the CNN was described as a heat map by class activation mapping (CAM). In the second model, which classified images into AMD with or without exudative changes, we compared the learning stabilization of models using or not using transfer learning.
resultsUsing the first CNN model, we could classify AMD and normal OCT images with 100% sensitivity, 91.8% specificity, and 99.0% accuracy. In the second, transfer-learning model, we could classify AMD as having or not having exudative changes, with 98.4% sensitivity, 88.3% specificity, and 93.9% accuracy. CAM successfully described the heat-map area on the OCT images. Including the transfer-learning model in the second model resulted in faster stabilization than when the transfer-learning model was not included.
conclusionTwo computational deep-learning models were developed and evaluated here; both models showed good performance. Automation of the interpretation process by using deep-learning models can save time and improve efficiency.
trial registrationNo15073.
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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.