Deep Learning-Based Detection of Reticular Pseudodrusen in Age-Related Macular Degeneration

Abstract

Background: Reticular pseudodrusen (RPD) signify a critical phenotype driving vision loss in age-related macular degeneration (AMD). This study sought to develop and externally test a deep learning (DL) model to detect RPD on optical coherence tomography (OCT) scans with expert-level performance. Methods: RPD were manually segmented in 9800 OCT B-scans from individuals enrolled in a multicentre randomised trial. A DL model for instance segmentation of RPD was developed and evaluated against four retinal specialists in an internal test dataset. The primary outcome was the performance of the DL model for detecting RPD in OCT volumes in five external test datasets compared to two retinal specia..