Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration.

ABSTRACT: Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. In this study, we compare the performance of retinal fundus images and genetic-information-based machine learning models for the prediction of late AMD. Using data from the Age-related Eye Disease Study, we built machine learning models with various combinations of genetic, socio-demographic/clinical, and retinal image data to predict late AMD using its severity and category in a single visit, in 2, 5, and 10 years. We compared their performance in sensitivity, specificity, accuracy, and unweighted kappa. The 2-year model based on retinal image and socio-demographic (S-D) parameters achieved a sensitivity of 91.34%, specificity of 84.49% while the same for genetic and S-D-parameters-based model w

SUBMITTER: Govindaiah A 

PROVIDER: S-EPMC8617775 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature