  A retinal trait, or phenotype, summarises a specific aspect of a retinal
image in a single number. This can then be used for further analyses, e.g. with
statistical methods. However, reducing an aspect of a complex image to a
single, meaningful number is challenging. Thus, methods for calculating retinal
traits tend to be complex, multi-step pipelines that can only be applied to
high quality images. This means that researchers often have to discard
substantial portions of the available data. We hypothesise that such pipelines
can be approximated with a single, simpler step that can be made robust to
common quality issues. We propose Deep Approximation of Retinal Traits (DART)
where a deep neural network is used predict the output of an existing pipeline
on high quality images from synthetically degraded versions of these images. We
demonstrate DART on retinal Fractal Dimension (FD) calculated by VAMPIRE, using
retinal images from UK Biobank that previous work identified as high quality.
Our method shows very high agreement with FD VAMPIRE on unseen test images
(Pearson r=0.9572). Even when those images are severely degraded, DART can
still recover an FD estimate that shows good agreement with FD VAMPIRE obtained
from the original images (Pearson r=0.8817). This suggests that our method
could enable researchers to discard fewer images in the future. Our method can
compute FD for over 1,000img/s using a single GPU. We consider these to be very
encouraging initial results and hope to develop this approach into a useful
tool for retinal analysis.
