ArticleNPJ digital medicine2020
Predicting risk of late age-related macular degeneration using deep learning.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 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.
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Who cites it
44 citing papers in PubMed, 65 citations in OpenAlex.
- An Updated Simplified Severity Scale for Age-Related Macular Degeneration Incorporating Reticular Pseudodrusen: Age-Related Eye Disease Study Report Number 42.Ophthalmology · 2024Trial
- Artificial intelligence in ophthalmology: From diagnostic accuracy to clinical application.World journal of methodology · 2026Review
- Reference Standard for Validation of Age-Related Macular Degeneration Screening Algorithms.Ophthalmology · 2026Article
- Advancing AMD Detection: Dataset Design and Deep Learning Optimization for Unconstrained Retinal Images.Vision (Basel, Switzerland) · 2026Article
- Article
- Mini-batch Estimation for Deep Cox Models: Statistical Foundations and Practical Guidance.Journal of the American Statistical Association · 2026Article
- A Datasheet for Age-Related Eye Disease Study 2 on the Database of Genotypes and Phenotypes.Ophthalmology science · 2026Article
- Leveraging large scale deep learning models for diagnosis and visual outcome prediction in retinitis pigmentosa.NPJ digital medicine · 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
- Article
- Treatment regimens for optimising outcomes in patients with neovascular age-related macular degeneration.Eye (London, England) · 2025Review
- Artificial intelligence in assessing progression of age-related macular degeneration.Eye (London, England) · 2025Review
- Evolutionary patterns and research frontiers of artificial intelligence in age-related macular degeneration: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2025Article
- tdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Artificial intelligence technology in ophthalmology public health: current applications and future directions.Frontiers in cell and developmental biology · 2025Review
- Tracing global progress: two decades of age-related macular degeneration research.International journal of ophthalmology · 2025Article
- Gallic Acid-Encapsulated PAMAM Dendrimers as an Antioxidant Delivery System for Controlled Release and Reduced Cytotoxicity against ARPE-19 Cells.Bioconjugate chemistry · 2024Article
- Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling.NPJ digital medicine · 2024Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
By 2040, age-related macular degeneration (AMD) will affect ~288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Although deep learning has shown promise in diagnosing/screening AMD using color fundus photographs, it remains difficult to predict individuals' risks of late AMD accurately. For both tasks, these initial deep learning attempts have remained largely unvalidated in independent cohorts. Here, we demonstrate how deep learning and survival analysis can predict the probability of progression to late AMD using 3298 participants (over 80,000 images) from the Age-Related Eye Disease Studies AREDS and AREDS2, the largest longitudinal clinical trials in AMD. When validated against an independent test data set of 601 participants, our model achieved high prognostic accuracy (5-year
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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.