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.

翻译：视网膜特性( phenotype) 将视网膜图象的某个特定方面归纳成一个单一数字。 然后, 它可以用来进行进一步的分析, 例如用统计方法。 但是, 将复杂图象的一个方面降为一个单一的、 有意义的数字是具有挑战性的。 因此, 计算视网膜特性的方法往往很复杂, 多步管道, 只能用于高质量的图像。 这意味着研究人员往往不得不丢弃大量可用数据。 我们假设, 这种管道可以用一个单一的、 更简单的步骤来接近于共同质量问题。 我们提议在使用深神经网络的情况下, 将一个复杂图象的某个方面降为单一的、 有意义的数字。 我们演示由 VAMPIRI 计算出来的视网状红线性红外线的多步管( FDRP), 可以考虑我们先前发现为高质量的正轨图。 我们的方法显示, 与FD VMPIRE 的一致程度非常高, 在使用原始的图象分析方法时, 将这种原始的图状显示一个退化法。