  Purpose: (1) To assess the performance of geometric deep learning (PointNet)
in diagnosing glaucoma from a single optical coherence tomography (OCT) 3D scan
of the optic nerve head (ONH); (2) To compare its performance to that obtained
with a standard 3D convolutional neural network (CNN), and with a gold-standard
glaucoma parameter, i.e. retinal nerve fiber layer (RNFL) thickness.
  Methods: 3D raster scans of the ONH were acquired with Spectralis OCT for 477
glaucoma and 2,296 non-glaucoma subjects at the Singapore National Eye Centre.
All volumes were automatically segmented using deep learning to identify 7
major neural and connective tissues including the RNFL, the prelamina, and the
lamina cribrosa (LC). Each ONH was then represented as a 3D point cloud with
1,000 points chosen randomly from all tissue boundaries. To simplify the
problem, all ONH point clouds were aligned with respect to the plane and center
of Bruch's membrane opening. Geometric deep learning (PointNet) was then used
to provide a glaucoma diagnosis from a single OCT point cloud. The performance
of our approach was compared to that obtained with a 3D CNN, and with RNFL
thickness.
  Results: PointNet was able to provide a robust glaucoma diagnosis solely from
the ONH represented as a 3D point cloud (AUC=95%). The performance of PointNet
was superior to that obtained with a standard 3D CNN (AUC=87%) and with that
obtained from RNFL thickness alone (AUC=80%).
  Discussion: We provide a proof-of-principle for the application of geometric
deep learning in the field of glaucoma. Our technique requires significantly
less information as input to perform better than a 3D CNN, and with an AUC
superior to that obtained from RNFL thickness alone. Geometric deep learning
may have wide applicability in the field of Ophthalmology.
