Ontology highlight
ABSTRACT: Objectives
Despite global research on early detection of age-related macular degeneration (AMD), not enough is being done for large-scale screening. Automated analysis of retinal images captured via smartphone presents a potential solution; however, to our knowledge, such an artificial intelligence (AI) system has not been evaluated. The study aimed to assess the performance of an AI algorithm in detecting referable AMD on images captured on a portable fundus camera.Design, setting
A retrospective image database from the Age-Related Eye Disease Study (AREDS) and target device was used.Participants
The algorithm was trained on two distinct data sets with macula-centric images: initially on 108,251 images (55% referable AMD) from AREDS and then fine-tuned on 1108 imag
SUBMITTER: Savoy FM
PROVIDER: S-EPMC11381639 | biostudies-literature | 2024 Sep
REPOSITORIES: biostudies-literature