ArticleTranslational vision science & technology2023
Geographic Atrophy Segmentation Using Multimodal Deep Learning.
Article in Translational vision science & technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 19 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Multicenter, Prospective Epidemiologic Study of the Progression of Geographic Atrophy Secondary to Age-Related Macular Degeneration
A Multicenter, Prospective Epidemiologic Study of The Progression of Geographic Atrophy Secondary to Age-Related Macular Degeneration
Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Trends and hotspots in the field of diabetic retinopathy imaging research from 2000-2023.Frontiers in medicine · 2024Pooled it
- Simultaneous Segmentation of Geographic Atrophy in Longitudinally Acquired Fundus Autofluorescence Images.Ophthalmology science · 2026Article
- Geographic Atrophy Structure-Function Relationships Based on Loss of OCT Outer Retinal Bands and Fundus Autofluorescence.Ophthalmology science · 2026Article
- SWAU-Net: Longitudinal Prediction of Geographic Atrophy via Sliding-Window Attention.Life (Basel, Switzerland) · 2026Article
- 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
- Dual-branch image projection network for geographic atrophy segmentation in retinal OCT images.Scientific reports · 2025Article
- Rethinking Clinical Trials in Age-Related Macular Degeneration: How AI-Based OCT Analysis Can Support Successful Outcomes.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Artificial intelligence in assessing progression of age-related macular degeneration.Eye (London, England) · 2025Review
- Longitudinal Changes of Visual Sensitivity With Geographic Atrophy Progression Assessed by Defect-Mapping Microperimetry.Investigative ophthalmology & visual science · 2024Article
- Topographic Clinical Insights From Deep Learning-Based Geographic Atrophy Progression Prediction.Translational vision science & technology · 2024Article
- Quantifying Geographic Atrophy in Age-Related Macular Degeneration: A Comparative Analysis Across 12 Deep Learning Models.Investigative ophthalmology & visual science · 2024Article
- AI-based support for optical coherence tomography in age-related macular degeneration.International journal of retina and vitreous · 2024Review
- Visual Sensitivity Loss in Geographic Atrophy: Structure-Function Evaluation Using Defect-Mapping Microperimetry.Investigative ophthalmology & visual science · 2024Article
- Longitudinal Comparison of Geographic Atrophy Enlargement Using Manual, Semiautomated, and Deep Learning Approaches.Ophthalmology scienceArticle
- Targeted Defect-Mapping Microperimetry in Geographic Atrophy: A Sensitive Functional Outcome Measure for Intervention Trials.Ophthalmology scienceArticle
- Customized Evaluation of Progressive Visual Sensitivity Loss in Geographic Atrophy to Improve the Power of Clinical Trials.Ophthalmology scienceArticle
- Strong versus Weak Data Labeling for Artificial Intelligence Algorithms in the Measurement of Geographic Atrophy.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To examine deep learning (DL)-based methods for accurate segmentation of geographic atrophy (GA) lesions using fundus autofluorescence (FAF) and near-infrared (NIR) images. Methods: This retrospective analysis utilized imaging data from study eyes of patients enrolled in Proxima A and B (NCT02479386; NCT02399072) natural history studies of GA. Two multimodal DL networks (UNet and YNet) were used to automatically segment GA lesions on FAF; segmentation accuracy was compared with annotations by experienced graders. The training data set comprised 940 image pairs (FAF and NIR) from 183 patients in Proxima B; the test data set comprised 497 image pairs from 154 patients in Proxima A. Dice coefficient scores, Bland-Altman plots, and Pearson correlation coefficient (r) were used to assess performance. Results: On the test set, Dice scores for the DL network to grader comparison ranged from 0.89 to 0.92 for screening visit; Dice score between graders was 0.94. GA lesion area correlations (r) for YNet versus grader, UNet versus grader, and between graders were 0.981, 0.959, and 0.995, respectively. Longitudinal GA lesion area enlargement correlations (r) for screening to 12 months (n = 53) were lower (0.741, 0.622, and 0.890, respectively) compared with the cross-sectional results at screening. Longitudinal correlations (r) from screening to 6 months (n = 77) were even lower (0.294, 0.248, and 0.686, respectively). Conclusions: Multimodal DL networks to segment GA lesions can produce accurate results comparable with expert graders. Translational Relevance: DL-based tools may support efficient and individualized assessment of patients with GA in clinical research and practice.
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Registered trials
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.