  Although unprecedented sensitivity and specificity values are reported,
recent glaucoma detection deep learning models lack in decision transparency.
Here, we propose a methodology that advances explainable deep learning in the
field of glaucoma detection and vertical cup-disc ratio (VCDR), an important
risk factor. We trained and evaluated deep learning models using fundus images
that underwent a certain cropping policy. We defined the crop radius as a
percentage of image size, centered on the optic nerve head (ONH), with an
equidistant spaced range from 10%-60% (ONH crop policy). The inverse of the
cropping mask was also applied (periphery crop policy). Trained models using
original images resulted in an area under the curve (AUC) of 0.94 [95% CI:
0.92-0.96] for glaucoma detection, and a coefficient of determination (R^2)
equal to 77% [95% CI: 0.77-0.79] for VCDR estimation. Models that were trained
on images with absence of the ONH are still able to obtain significant
performance (0.88 [95% CI: 0.85-0.90] AUC for glaucoma detection and 37% [95%
CI: 0.35-0.40] R^2 score for VCDR estimation in the most extreme setup of 60%
ONH crop). Our findings provide the first irrefutable evidence that deep
learning can detect glaucoma from fundus image regions outside the ONH.
