ArticleBiomedical optics express2016
Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images.
Article in Biomedical optics express, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
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Who cites it
36 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
- A review of optimization strategies for deep and machine learning in diabetic macular edema.Frontiers in artificial intelligence · 2026Pooled it
- Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis.Eye (London, England) · 2022Pooled it
- AMD-UPerNet: a tool for retinal layer and fluid assessment in age-related macular degeneration.BMC medical imaging · 2026Article
- A novel approach for automatic classification of macular degeneration OCT images.Scientific reports · 2024Article
- A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD).Bioengineering (Basel, Switzerland) · 2024Review
- A new intelligent system based deep learning to detect DME and AMD in OCT images.International ophthalmology · 2024Article
- Artificial intelligence applications in ophthalmic optical coherence tomography: a 12-year bibliometric analysis.International journal of ophthalmology · 2024Article
- 3D-FVS: construction and application of three-dimensional fundus vascular structure model based on single image features.Eye (London, England) · 2023Article
- Drusen-aware model for age-related macular degeneration recognition.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2023Article
- Deep learning model for automatic image quality assessment in PET.BMC medical imaging · 2023Article
- Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis.Ophthalmology science · 2023Article
- RVM-GSM: Classification of OCT Images of Genitourinary Syndrome of Menopause Based on Integrated Model of Local-Global Information Pattern.Bioengineering (Basel, Switzerland) · 2023Article
- Distinctions between Choroidal Neovascularization and Age Macular Degeneration in Ocular Disease Predictions via Multi-Size Kernels ξcho-Weighted Median Patterns.Diagnostics (Basel, Switzerland) · 2023Article
- Classification of Retinal Diseases in Optical Coherence Tomography Images Using Artificial Intelligence and Firefly Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Review
- Artificial intelligence method based on multi-feature fusion for automatic macular edema (ME) classification on spectral-domain optical coherence tomography (SD-OCT) images.Frontiers in neuroscience · 2023Article
- Application of Deep Learning for Automated Detection of Polypoidal Choroidal Vasculopathy in Spectral Domain Optical Coherence Tomography.Translational vision science & technology · 2022Article
- Evaluating machine learning classifiers for glaucoma referral decision support in primary care settings.Scientific reports · 2022Article
- Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-lethal macular diseases greatly impact patients' life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm. And the best model based on the sequential minimal optimization (SMO) algorithm achieved 99.3% in the overall accuracy for the three classes of samples.
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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.