ArticleAmerican journal of ophthalmology2020
Age-related Macular Degeneration: Nutrition, Genes and Deep Learning-The LXXVI Edward Jackson Memorial Lecture.
Article in American journal of ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 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.
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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
8 citing papers in PubMed, 31 citations in OpenAlex.
- Feasibility of Multimodal Deep Learning for Automated Staging of Familial Exudative Vitreoretinopathy Using Color Fundus Photographs and Fluorescein Angiography.Diagnostics (Basel, Switzerland) · 2025Article
- Modifiable Lifestyle Risk Factors and Strategies for Slowing the Progression of Age-Related Macular Degeneration.Vision (Basel, Switzerland) · 2025Review
- Immunogenetic and Environmental Factors in Age-Related Macular Disease.International journal of molecular sciences · 2024Review
- Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system.The EPMA journal · 2024Article
- Perspectives from clinical trials: is geographic atrophy one disease?Eye (London, England) · 2023Review
- Automation of Macular Degeneration Classification in the AREDS Dataset, Using a Novel Neural Network Design.Clinical ophthalmology (Auckland, N.Z.) · 2023Article
- Therapeutic Approaches for Age-Related Macular Degeneration.International journal of molecular sciences · 2022Review
- Review
Corrections and comments
- Erratum issued
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
purposeTo evaluate the importance of nutritional supplements, dietary pattern, and genetic associations in age-related macular degeneration (AMD); and to discuss the technique of artificial intelligence/deep learning to potentially enhance research in detecting and classifying AMD.
designRetrospective literature review.
methodsTo review the studies of both prospective and retrospective (post hoc) analyses of nutrition, genetic variants, and deep learning in AMD in both the Age-Related Eye Disease Study (AREDS) and AREDS2.
resultsIn addition to demonstrating the beneficial effects of the AREDS and AREDS2 supplements of antioxidant vitamins and zinc (plus copper) for reducing the risk of progression to late AMD, these 2 studies also confirmed the importance of high adherence to Mediterranean diet in reducing progression of AMD in persons with varying severity of disease. In persons with the protective genetic alleles of complement factor H (CFH), the Mediterranean diet had further beneficial effect. However, despite the genetic association with AMD progression, prediction models found genetic information added little to the high predictive value of baseline severity of AMD for disease progression. The technique of deep learning, an arm of artificial intelligence, using color fundus photographs from AREDS/AREDS2 was superior in some cases and noninferior in others to clinical human grading (retinal specialists) and to the gold standard of the certified reading center graders.
conclusionsCounseling individuals affected with AMD regarding the use of the AREDS2 supplements and the beneficial association of the Mediterranean diet is an important public health message. Although genetic testing is important in research, it is not recommended for prediction of disease or to guide therapies and/or dietary interventions in AMD. Techniques in deep learning hold great promise, but further prospective research is required to validate the use of this technique to provide improvement in accuracy and sensitivity/specificity in clinical research and medical management of patients with AMD.
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