  The lack of reliable biomarkers makes predicting the conversion from
intermediate to neovascular age-related macular degeneration (iAMD, nAMD) a
challenging task. We develop a Deep Learning (DL) model to predict the future
risk 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 monitor
AMD 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 from
different visits and involves morphing the scan from the previous visit to the
next. The Decoder predicts the transformation for morphing and ensures a smooth
feature manifold that can generate intermediate scans between visits through
linear interpolation. Next, the Morph-SSL trained features are input to a
Classifier which is trained in a supervised manner to model the cumulative
probability distribution of the time to conversion with a sigmoidal function.
Morph-SSL was trained on unlabelled scans of 399 eyes (3570 visits). The
Classifier was evaluated with a five-fold cross-validation on 2418 scans from
343 eyes with clinical labels of the conversion date. The Morph-SSL features
achieved an AUC of 0.766 in predicting the conversion to nAMD within the next 6
months, outperforming the same network when trained end-to-end from scratch or
pre-trained with popular SSL methods. Automated prediction of the future risk
of nAMD onset can enable timely treatment and individualized AMD management.
