  Previous approaches using deep learning algorithms to classify glaucomatous
damage on fundus photographs have been limited by the requirement for human
labeling of a reference training set. We propose a new approach using
spectral-domain optical coherence tomography (SDOCT) data to train a deep
learning algorithm to quantify glaucomatous structural damage on optic disc
photographs. The dataset included 32,820 pairs of optic disc photos and SDOCT
retinal nerve fiber layer (RNFL) scans from 2,312 eyes of 1,198 subjects. A
deep learning convolutional neural network was trained to assess optic disc
photographs and predict SDOCT average RNFL thickness. The performance of the
algorithm was evaluated in an independent test sample. The mean prediction of
average RNFL thickness from all 6,292 optic disc photos in the test set was
83.3$\pm$14.5 $\mu$m, whereas the mean average RNFL thickness from all
corresponding SDOCT scans was 82.5$\pm$16.8 $\mu$m (P = 0.164). There was a
very strong correlation between predicted and observed RNFL thickness values (r
= 0.832; P<0.001), with mean absolute error of the predictions of 7.39 $\mu$m.
The areas under the receiver operating characteristic curves for discriminating
glaucoma from healthy eyes with the deep learning predictions and actual SDOCT
measurements were 0.944 (95$\%$ CI: 0.912- 0.966) and 0.940 (95$\%$ CI: 0.902 -
0.966), respectively (P = 0.724). In conclusion, we introduced a novel deep
learning approach to assess optic disc photographs and provide quantitative
information about the amount of neural damage. This approach could potentially
be used to diagnose and stage glaucomatous damage from optic disc photographs.
