  The optic nerve head (ONH) typically experiences complex neural- and
connective-tissue structural changes with the development and progression of
glaucoma, and monitoring these changes could be critical for improved diagnosis
and prognosis in the glaucoma clinic. The gold-standard technique to assess
structural changes of the ONH clinically is optical coherence tomography (OCT).
However, OCT is limited to the measurement of a few hand-engineered parameters,
such as the thickness of the retinal nerve fiber layer (RNFL), and has not yet
been qualified as a stand-alone device for glaucoma diagnosis and prognosis
applications. We argue this is because the vast amount of information available
in a 3D OCT scan of the ONH has not been fully exploited. In this study we
propose a deep learning approach that can: \textbf{(1)} fully exploit
information from an OCT scan of the ONH; \textbf{(2)} describe the structural
phenotype of the glaucomatous ONH; and that can \textbf{(3)} be used as a
robust glaucoma diagnosis tool. Specifically, the structural features
identified by our algorithm were found to be related to clinical observations
of glaucoma. The diagnostic accuracy from these structural features was $92.0
\pm 2.3 \%$ with a sensitivity of $90.0 \pm 2.4 \% $ (at $95 \%$ specificity).
By changing their magnitudes in steps, we were able to reveal how the
morphology of the ONH changes as one transitions from a `non-glaucoma' to a
`glaucoma' condition. We believe our work may have strong clinical implication
for our understanding of glaucoma pathogenesis, and could be improved in the
future to also predict future loss of vision.
