Deep-learning-based Prediction of Late Age-Related Macular Degeneration Progression.

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

SUBMITTER: Yan Q 

PROVIDER: S-EPMC7153739 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature