  Purpose: (1) To develop a deep learning algorithm to identify major tissue
structures of the optic nerve head (ONH) in 3D optical coherence tomography
(OCT) scans; (2) to exploit such information to robustly differentiate among
healthy, optic disc drusen (ODD), and papilledema ONHs.
  It was a cross-sectional comparative study with confirmed ODD (105 eyes),
papilledema due to high intracranial pressure (51 eyes), and healthy controls
(100 eyes). 3D scans of the ONHs were acquired using OCT, then processed to
improve deep-tissue visibility. At first, a deep learning algorithm was
developed using 984 B-scans (from 130 eyes) in order to identify: major
neural/connective tissues, and ODD regions. The performance of our algorithm
was assessed using the Dice coefficient (DC). In a 2nd step, a classification
algorithm (random forest) was designed using 150 OCT volumes to perform 3-class
classifications (1: ODD, 2: papilledema, 3: healthy) strictly from their drusen
and prelamina swelling scores (derived from the segmentations). To assess
performance, we reported the area under the receiver operating characteristic
curves (AUCs) for each class.
  Our segmentation algorithm was able to isolate neural and connective tissues,
and ODD regions whenever present. This was confirmed by an average DC of
0.93$\pm$0.03 on the test set, corresponding to good performance.
Classification was achieved with high AUCs, i.e. 0.99$\pm$0.01 for the
detection of ODD, 0.99 $\pm$ 0.01 for the detection of papilledema, and
0.98$\pm$0.02 for the detection of healthy ONHs.
  Our AI approach accurately discriminated ODD from papilledema, using a single
OCT scan. Our classification performance was excellent, with the caveat that
validation in a much larger population is warranted. Our approach may have the
potential to establish OCT as the mainstay of diagnostic imaging in
neuro-ophthalmology.
