Drusen volume and reticular pseudodrusen volume from optical coherence tomography with deep learning as risk factors for progression to late age-related macular degeneration in eyes with reticular pseudodrusen and contralateral macular neovascularisation.

Abstract

To implement a deep learning-based segmentation algorithm to quantify reticular pseudodrusen (RPD) and drusen volumes on optical coherence tomography (OCT) and investigate their association with progression to late age-related macular degeneration (AMD). A retrospective analysis included study eyes with RPD and contralateral neovascular AMD using 6×6 mm macular OCT (Solix; Visionix/Optovue, Inc). Automated segmentation quantified RPD and drusen volumes, including large drusen and drusenoid pigment epithelial detachment (PED), and late AMD development was evaluated over 2 years. Associations between baseline volumetric biomarkers and progression were evaluated using Cox proportional hazards models. Fifty-one eyes (mean age 74.9±7.65 years) were included. The median (IQR) baseline RPD volume was 0.018 mm³ (0.004-0.52) and total drusen volume was 0.009 mm³ (0.001-0.064). Over 24.2±1.20 months, late AMD developed in 20 eyes (39.2%). In multivariable Cox regression models adjusted for age, each 0.01 mm³ increase in baseline RPD volume (HR: 1.082; p=0.002) and total drusen volume (HR: 1.080; p<0.001) were associated with progression to late AMD. In an exploratory drusen subtype analysis, progression to late AMD was independently associated with baseline RPD volume, large drusen volume and drusenoid PED volume. The deep learning-based volumetric segmentation tool allows OCT-derived automated volume quantification of different types of drusen based on OCT. In eyes with RPD and contralateral neovascular AMD, greater RPD volume and large drusen and/or drusenoid PED volume carried greater risk of developing late AMD in 2 years.