Purpose To evaluate longitudinally volume changes in inner and outer retinal layers in early and intermediate age-related macular degeneration (AMD) compared to healthy control eyes using optical coherence tomography (OCT). Methods 71 eyes with AMD and 31 control eyes were imaged at two time points: baseline and after 2 years. Automated OCT layer segmentation was performed using Orion TM. This software is able to measure volumes of retinal layers with distinct boundaries including Retinal Nerve Fibre Layer (RNFL), Ganglion Cell-Inner Plexiform Layer (GCIPL), Inner Nuclear Layer (INL), Outer Plexiform Layer (OPL), Outer Nuclear Layer (ONL), Photoreceptors (PR) and Retinal Pigment Epithelium-Bruch's Membrane complex (RPE-BM). The mean retinal layer volumes and volume changes at 2 years were compared between groups. Results Mean GCIPL and INL volumes were lower, while PR and RPE-BM volumes were higher in AMD eyes than controls at baseline (all P < 0.05) and year 2 (all P < 0.05). In AMD eyes, RNFL and ONL volumes decreased by 0.0232 (P = 0.033) and 0.0851 (P = 0.001), respectively. In contrast, OPL and RPE-BM volumes increased in AMD eyes by 0.0391 (P = 0.000) and 0.0209 (P = 0.000) respectively. Moreover, there were significant differences in longitudinal volume change of OPL (P = 0.02), ONL (P = 0.008) and RPE-BM (P = 0.02) between AMD eyes and controls. Conclusions There were abnormal retinal layer volumes and volume changes in eyes with early and intermediate AMD.

PURPOSE. To develop and assess a method for predicting the likelihood of converting from early/intermediate to advanced wet age-related macular degeneration (AMD) using optical coherence tomography (OCT) imaging and methods of deep learning.METHODS. Seventy-one eyes of 71 patients with confirmed early/intermediate AMD with contralateral wet AMD were imaged with OCT three times over 2 years (baseline, year 1, year 2). These eyes were divided into two groups: eyes that had not converted to wet AMD (n ¼ 40) at year 2 and those that had (n ¼ 31). Two deep convolutional neural networks (CNN) were evaluated using 5-fold cross validation on the OCT data at baseline to attempt to predict which eyes would convert to advanced AMD at year 2: (1) VGG16, a popular CNN for image recognition was fine-tuned, and (2) a novel, simplified CNN architecture was trained from scratch. Preprocessing was added in the form of a segmentation-based normalization to reduce variance in the data and improve performance. RESULTS.Our new architecture, AMDnet, with preprocessing, achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89 at the B-scan level and 0.91 for volumes. Results for VGG16, an established CNN architecture, with preprocessing were 0.82 for B-scans/0.87 for volumes versus 0.66 for B-scans/0.69 for volumes without preprocessing. CONCLUSIONS.A CNN with layer segmentation-based preprocessing shows strong predictive power for the progression of early/intermediate AMD to advanced AMD. Use of the preprocessing was shown to improve performance regardless of the network architecture.

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