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The lack of reliable biomarkers makes predicting the conversion fromintermediate to neovascular age-related macular degeneration (iAMD, nAMD) achallenging task. We develop a Deep Learning (DL) model to predict the futurerisk of conversion of an eye from iAMD to nAMD from its current OCT scan.Although eye clinics generate vast amounts of longitudinal OCT scans to monitorAMD progression, only a small subset can be manually labeled for supervised DL.To address this issue, we propose Morph-SSL, a novel Self-supervised Learning(SSL) method for longitudinal data. It uses pairs of unlabelled OCT scans fromdifferent visits and involves morphing the scan from the previous visit to thenext. The Decoder predicts the transformation for morphing and ensures a smoothfeature manifold that can generate intermediate scans between visits throughlinear interpolation. Next, the Morph-SSL trained features are input to aClassifier which is trained in a supervised manner to model the cumulativeprobability distribution of the time to conversion with a sigmoidal function.Morph-SSL was trained on unlabelled scans of 399 eyes (3570 visits). TheClassifier was evaluated with a five-fold cross-validation on 2418 scans from343 eyes with clinical labels of the conversion date. The Morph-SSL featuresachieved an AUC of 0.766 in predicting the conversion to nAMD within the next 6months, outperforming the same network when trained end-to-end from scratch orpre-trained with popular SSL methods. Automated prediction of the future riskof nAMD onset can enable timely treatment and individualized AMD management.

Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT