ArticleScientific reports2021
Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography.
Article in Scientific reports, 2021. 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, 29 citations in OpenAlex.
- Detection and Management of Geographic Atrophy Secondary to Age-Related Macular Degeneration Using Noninvasive Retinal Images and Artificial Intelligence: Systematic Review.Journal of medical Internet research · 2025Pooled it
- A deep learning model for automated identification of age-related macular degeneration atrophy.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Review
- Exploratory study on a novel automatic quantification software by artificial intelligence for geographic atrophy associated to age-related macular degeneration.International journal of retina and vitreous · 2026Article
- Deep Learning-Based Choroidal Boundary Detection in Geographic Atrophy Using Spectral-Domain Optical Coherence Tomography.Diagnostics (Basel, Switzerland) · 2026Article
- Geographic Atrophy Progression in Clinical Practice Before and After Pegcetacoplan Treatment.Vision (Basel, Switzerland) · 2025Article
- Deep Learning-Based Segmentation of Geographic Atrophy: A Multi-Center, Multi-Device Validation in a Real-World Clinical Cohort.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence Approaches for Geographic Atrophy Segmentation: A Systematic Review and Meta-Analysis.Bioengineering (Basel, Switzerland) · 2025Review
- Rethinking Clinical Trials in Age-Related Macular Degeneration: How AI-Based OCT Analysis Can Support Successful Outcomes.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Early Detection of Macular Atrophy Automated Through 2D and 3D Unet Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Artificial intelligence in age-related macular degeneration: state of the art and recent updates.BMC ophthalmology · 2024Review
- Dry age-related macular degeneration classification from optical coherence tomography images based on ensemble deep learning architecture.Frontiers in medicine · 2024Article
- Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5).Scientific reports · 2023Article
- Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography.Scientific reports · 2023Article
- Clinical validation for automated geographic atrophy monitoring on OCT under complement inhibitory treatment.Scientific reports · 2023Article
- On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images.Bioengineering (Basel, Switzerland) · 2023Review
- Deep ensemble learning for automated non-advanced AMD classification using optimized retinal layer segmentation and SD-OCT scans.Computers in biology and medicine · 2023Article
- Machine Learning-Based Automated Detection and Quantification of Geographic Atrophy and Hypertransmission Defects Using Spectral Domain Optical Coherence Tomography.Journal of personalized medicine · 2022Article
- Personalized Atrophy Risk Mapping in Age-Related Macular Degeneration.Translational vision science & technology · 2021Article
- Extent of Complete Retinal Pigment Epithelial and Outer Retinal Atrophy with Foveal Center Involvement is Associated with Visual Acuity.Ophthalmology scienceArticle
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Age-related macular degeneration (AMD) is a progressive retinal disease, causing vision loss. A more detailed characterization of its atrophic form became possible thanks to the introduction of Optical Coherence Tomography (OCT). However, manual atrophy quantification in 3D retinal scans is a tedious task and prevents taking full advantage of the accurate retina depiction. In this study we developed a fully automated algorithm segmenting Retinal Pigment Epithelial and Outer Retinal Atrophy (RORA) in dry AMD on macular OCT. 62 SD-OCT scans from eyes with atrophic AMD (57 patients) were collected and split into train and test sets. The training set was used to develop a Convolutional Neural Network (CNN). The performance of the algorithm was established by cross validation and comparison to the test set with ground-truth annotated by two graders. Additionally, the effect of using retinal layer segmentation during training was investigated. The algorithm achieved mean Dice scores of 0.881 and 0.844, sensitivity of 0.850 and 0.915 and precision of 0.928 and 0.799 in comparison with Expert 1 and Expert 2, respectively. Using retinal layer segmentation improved the model performance. The proposed model identified RORA with performance matching human experts. It has a potential to rapidly identify atrophy with high consistency.
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