ReviewTranslational vision science & technology2020
Artificial Intelligence Algorithms for Analysis of Geographic Atrophy: A Review and Evaluation.
Review in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 50 citations in OpenAlex.
- Oral Antioxidant and Lutein/Zeaxanthin Supplements Slow Geographic Atrophy Progression to the Fovea in Age-Related Macular Degeneration.Ophthalmology · 2025Trial
- Deep-learning automated quantification of longitudinal OCT scans demonstrates reduced RPE loss rate, preservation of intact macular area and predictive value of isolated photoreceptor degeneration in geographic atrophy patients receiving C3 inhibition treatment.The British journal of ophthalmology · 2024Trial
- Review
- Geographic Atrophy Progression in Clinical Practice Before and After Pegcetacoplan Treatment.Vision (Basel, Switzerland) · 2025Article
- Revolutionizing AMD detection Bi model CNNs and hybrid feature selection for automated grading.Scientific reports · 2025Article
- Comparison of effectiveness of geographic atrophy automatic segmentation with different imaging methods.Eye (London, England) · 2025Article
- Artificial Intelligence Approaches for Geographic Atrophy Segmentation: A Systematic Review and Meta-Analysis.Bioengineering (Basel, Switzerland) · 2025Review
- Combination of optical coherence tomography-derived shape and texture features are associated with development of sub-foveal geographic atrophy in dry AMD.Scientific reports · 2024Article
- Quantifying Geographic Atrophy in Age-Related Macular Degeneration: A Comparative Analysis Across 12 Deep Learning Models.Investigative ophthalmology & visual science · 2024Article
- Inter-rater reliability in labeling quality and pathological features of retinal OCT scans: A customized annotation software approach.PloS one · 2024Article
- Explainable artificial intelligence model for the detection of geographic atrophy using colour retinal photographs.BMJ open ophthalmology · 2023Article
- Deep-GA-Net for Accurate and Explainable Detection of Geographic Atrophy on OCT Scans.Ophthalmology science · 2023Article
- Novel Fractal-Based Sub-RPE Compartment OCT Radiomics Biomarkers Are Associated With Subfoveal Geographic Atrophy in Dry AMD.IEEE transactions on bio-medical engineering · 2023Article
- Geographic Atrophy Segmentation Using Multimodal Deep Learning.Translational vision science & technology · 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
- Application of Machine Learning to Ranking Predictors of Anti-VEGF Response.Life (Basel, Switzerland) · 2022Article
- Artificial intelligence-based strategies to identify patient populations and advance analysis in age-related macular degeneration clinical trials.Experimental eye research · 2022Article
- Local Complement Inhibition for Geographic Atrophy in Age-Related Macular Degeneration: Prospects, Challenges, and Unanswered Questions.Ophthalmology science · 2021Article
- Progression of Geographic Atrophy: Epistemic Uncertainties Affecting Mathematical Models and Machine Learning.Translational vision science & technology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The purpose of this study was to summarize and evaluate artificial intelligence (AI) algorithms used in geographic atrophy (GA) diagnostic processes (e.g. isolating lesions or disease progression). Methods: The search strategy and selection of publications were both conducted in accordance with the Preferred of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Web of Science were used to extract literary data. The algorithms were summarized by objective, performance, and scope of coverage of GA diagnosis (e.g. lesion automation and GA progression). Results: Twenty-seven studies were identified for this review. A total of 18 publications focused on lesion segmentation only, 2 were designed to detect and classify GA, 2 were designed to predict future overall GA progression, 3 focused on prediction of future spatial GA progression, and 2 focused on prediction of visual function in GA. GA-related algorithms reported sensitivities from 0.47 to 0.98, specificities from 0.73 to 0.99, accuracies from 0.42 to 0.995, and Dice coefficients from 0.66 to 0.89. Conclusions: Current GA-AI publications have a predominant focus on lesion segmentation and a minor focus on classification and progression analysis. AI could be applied to other facets of GA diagnoses, such as understanding the role of hyperfluorescent areas in GA. Using AI for GA has several advantages, including improved diagnostic accuracy and faster processing speeds. Translational Relevance: AI can be used to quantify GA lesions and therefore allows one to impute visual function and quality-of-life. However, there is a need for the development of reliable and objective models and software to predict the rate of GA progression and to quantify improvements due to interventions.
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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.