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Reticular pseudodrusen (RPD) are subretinal drusenoid deposits that represent an important disease feature in age-related macular degeneration (AMD). RPD are of particular interest because their presence is a strong predictor of progression to advanced AMD. RPD features can be characterized using volumetric spectral-domain optical coherence tomography (SD-OCT). In this work, we curated a dataset from the Age-Related Eye Diseases Study 2 (AREDS2) ancillary OCT study. The dataset included 826 SD-OCT scans, with RPD present in 222 SD-OCT scans. Binary RPD labels were transferred from fundus autofluorescence (FAF) images taken at the same visits as the SD-OCT scans. The dataset was split at the participant level into training (70%), validation (10%), and test sets (20%). We proposed a 3D classification network to detect RPD from SD-OCT scans. We compared it to a baseline 2D network with average bagging and a 3D network with multi-tasking. The proposed network achieved the highest accuracy of 0.7784, area under receiver characteristic operating curve of 0.8689, and mean average precision of 0.7706 for detecting RPD from SD-OCT scans.
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