  Refractive error, one of the leading cause of visual impairment, can be
corrected by simple interventions like prescribing eyeglasses. We trained a
deep learning algorithm to predict refractive error from the fundus photographs
from participants in the UK Biobank cohort, which were 45 degree field of view
images and the AREDS clinical trial, which contained 30 degree field of view
images. Our model use the "attention" method to identify features that are
correlated with refractive error. Mean absolute error (MAE) of the algorithm's
prediction compared to the refractive error obtained in the AREDS and UK
Biobank. The resulting algorithm had a MAE of 0.56 diopters (95% CI: 0.55-0.56)
for estimating spherical equivalent on the UK Biobank dataset and 0.91 diopters
(95% CI: 0.89-0.92) for the AREDS dataset. The baseline expected MAE (obtained
by simply predicting the mean of this population) was 1.81 diopters (95% CI:
1.79-1.84) for UK Biobank and 1.63 (95% CI: 1.60-1.67) for AREDS. Attention
maps suggested that the foveal region was one of the most important areas used
by the algorithm to make this prediction, though other regions also contribute
to the prediction. The ability to estimate refractive error with high accuracy
from retinal fundus photos has not been previously known and demonstrates that
deep learning can be applied to make novel predictions from medical images.
Given that several groups have recently shown that it is feasible to obtain
retinal fundus photos using mobile phones and inexpensive attachments, this
work may be particularly relevant in regions of the world where autorefractors
may not be readily available.
