  In this paper, we seek a clinically-relevant latent code for representing the
spectrum of macular disease. Towards this end, we construct retina-VAE, a
variational autoencoder-based model that accepts a patient profile vector
(pVec) as input. The pVec components include clinical exam findings and
demographic information. We evaluate the model on a subspectrum of the retinal
maculopathies, in particular, exudative age-related macular degeneration,
central serous chorioretinopathy, and polypoidal choroidal vasculopathy. For
these three maculopathies, a database of 3000 6-dimensional pVecs (1000 each)
was synthetically generated based on known disease statistics in the
literature. The database was then used to train the VAE and generate latent
vector representations. We found training performance to be best for a
3-dimensional latent vector architecture compared to 2 or 4 dimensional
latents. Additionally, for the 3D latent architecture, we discovered that the
resulting latent vectors were strongly clustered spontaneously into one of 14
clusters. Kmeans was then used only to identify members of each cluster and to
inspect cluster properties. These clusters suggest underlying disease subtypes
which may potentially respond better or worse to particular pharmaceutical
treatments such as anti-vascular endothelial growth factor variants. The
retina-VAE framework will potentially yield new fundamental insights into the
mechanisms and manifestations of disease. And will potentially facilitate the
development of personalized pharmaceuticals and gene therapies.
