Bilateral cases of branch retinal vein occlusions (BRVO) are infrequent and often related to systemic disease. A 72-year-old man with biochemical recurrence of prostate cancer was referred for decreased vision in his left eye. Fundus examination and fluorescein angiography disclosed bilateral BRVO with patches of peripheral non-perfusion and macular oedema in the left eye. A systemic work up revealed elevated fibrinogen and reduced free protein S antigen, consistent with an underlying hypercoagulable state. Cancer is a well-known cause of hypercoagulability. We report the first case of bilateral BRVO related to biochemical recurrence of prostate cancer and a proven coagulation derangement.

No abstract

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.

No abstract