Age-related Macular Degeneration: Nutrition, Genes and Deep Learning—The LXXVI Edward Jackson Memorial Lecture - 19/07/20

Doi : 10.1016/j.ajo.2020.05.042

Emily Y. Chew, MD ^⁎
Clinical Trials Branch, Division of Epidemiology and Clinical Applications, National Eye Institute/National Institutes of Health, Bethesda, Maryland, USA

^∗Inquiries to Emily Y. Chew, Division of Epidemiology and Clinical Applications, National Eye Institute/National Institutes of Health, Building 10, CRC, Room 3-2531, 10 Center Dr, MSC 1204, Bethesda, MD 20892-1204, USADivision of Epidemiology and Clinical ApplicationsNational Eye Institute/National Institutes of HealthBuilding 10, CRC, Room 3-2531, 10 Center Dr, MSC 1204BethesdaMD20892-1204USA

Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le Sunday 19 July 2020
Cet article a été publié dans un numéro de la revue, cliquez ici pour y accéder

Abstract

Purpose

To 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.

Design

Retrospective literature review.

Methods

To 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.

Results

In 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.

Conclusions

Counseling 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.

Le texte complet de cet article est disponible en PDF.