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Previous approaches using deep learning algorithms to classify glaucomatousdamage on fundus photographs have been limited by the requirement for humanlabeling of a reference training set. We propose a new approach usingspectral-domain optical coherence tomography (SDOCT) data to train a deeplearning algorithm to quantify glaucomatous structural damage on optic discphotographs. The dataset included 32,820 pairs of optic disc photos and SDOCTretinal nerve fiber layer (RNFL) scans from 2,312 eyes of 1,198 subjects. Adeep learning convolutional neural network was trained to assess optic discphotographs and predict SDOCT average RNFL thickness. The performance of thealgorithm was evaluated in an independent test sample. The mean prediction ofaverage RNFL thickness from all 6,292 optic disc photos in the test set was83.3$\pm$14.5 $\mu$m, whereas the mean average RNFL thickness from allcorresponding SDOCT scans was 82.5$\pm$16.8 $\mu$m (P = 0.164). There was avery 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 discriminatingglaucoma from healthy eyes with the deep learning predictions and actual SDOCTmeasurements 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 deeplearning approach to assess optic disc photographs and provide quantitativeinformation about the amount of neural damage. This approach could potentiallybe used to diagnose and stage glaucomatous damage from optic disc photographs.

From Machine to Machine: An OCT-trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs