ArticleNature machine intelligence2020
Deep-learning-based Prediction of Late Age-Related Macular Degeneration Progression.
Article in Nature machine intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
65 citing papers in PubMed, 1 synthesis or guideline pooled it, 134 citations in OpenAlex.
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Self-Supervised Feature Learning and Phenotyping for Assessing Age-Related Macular Degeneration Using Retinal Fundus Images.Ophthalmology. Retina · 2022Trial
- Modular multi-task deep learning framework for prediction of treatment initiation in neovascular age-related macular degeneration modular AI for NVAMD treatment initiation study-MANTIS.The British journal of ophthalmology · 2026Article
- VMDiff: A novel hybrid CNN-Mamba generative framework for retinal phenotype image enhancement.Medical & biological engineering & computing · 2026Article
- Artificial Intelligence in Ophthalmology: Current Status, Challenges, and Future Perspectives.Health care science · 2026Review
- Article
- Artificial Intelligence in Vitreoretinal Surgery: A Systematic Review of Current Applications and Future Directions.Ophthalmology and therapy · 2026Review
- AI-based prediction of best-corrected visual acuity in patients with multiple retinal diseases using multimodal medical imaging.The British journal of ophthalmology · 2026Article
- Multivariable Prognostic Models for Age-Related Macular Degeneration: A Systematic Review.Journal of ophthalmology · 2026Review
- AMD-Mamba: A Phenotype-Aware Multi-modal Framework for Robust AMD Prognosis.Machine learning in medical imaging. MLMI (Workshop) · 2026Article
- Artificial intelligence-driven diagnosis for age-related macular degeneration bridging pathology and engineering: a survey.International ophthalmology · 2025Review
- Accelerated Biological Aging and Genetic Pleiotropy in Age-Related Eye Diseases: A Population-Based Cohort and Integrative Genetic Analysis.Investigative ophthalmology & visual science · 2025Article
- Article
- Interpretable longitudinal glaucoma visual field estimation deep learning system from fundus images and clinical narratives.NPJ digital medicine · 2025Article
- Genetic insights into foveal morphology and its associations with pigmentation and age-related macular degeneration.medRxiv : the preprint server for health sciences · 2025Article
- Treatment regimens for optimising outcomes in patients with neovascular age-related macular degeneration.Eye (London, England) · 2025Review
- Deep Learning Approach Predicts Longitudinal Retinal Nerve Fiber Layer Thickness Changes.Bioengineering (Basel, Switzerland) · 2025Article
- tdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Machine learning models for risk prediction of age-related macular degeneration in Fujian eye study.PloS one · 2025Article
- Nomogram-based prediction of continued axial elongation in children undergoing orthokeratology.Frontiers in medicine · 2025Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Both genetic and environmental factors influence the etiology of age-related macular degeneration (AMD), a leading cause of blindness. AMD severity is primarily measured by fundus images and recently developed machine learning methods can successfully predict AMD progression using image data. However, none of these methods have utilized both genetic and image data for predicting AMD progression. Here we jointly used genotypes and fundus images to predict an eye as having progressed to late AMD with a modified deep convolutional neural network (CNN). In total, we used 31,262 fundus images and 52 AMD-associated genetic variants from 1,351 subjects from the Age-Related Eye Disease Study (AREDS) with disease severity phenotypes and fundus images available at baseline and follow-up visits over a period of 12 years. Our results showed that fundus images coupled with genotypes could predict late AMD progression with an averaged area under the curve (AUC) value of 0.85 (95%CI: 0.83-0.86). The results using fundus images alone showed an averaged AUC of 0.81 (95%CI: 0.80-0.83). We implemented our model in a cloud-based application for individual risk assessment.
Identifiers
What OpenQuestion holds
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.