  Glaucoma is the leading cause of preventable, irreversible blindness
world-wide. The disease can remain asymptomatic until severe, and an estimated
50%-90% of people with glaucoma remain undiagnosed. Glaucoma screening is
recommended for early detection and treatment. A cost-effective tool to detect
glaucoma could expand screening access to a much larger patient population, but
such a tool is currently unavailable. We trained a deep learning algorithm
using a retrospective dataset of 86,618 images, assessed for glaucomatous optic
nerve head features and referable glaucomatous optic neuropathy (GON). The
algorithm was validated using 3 datasets. For referable GON, the algorithm had
an AUC of 0.945 (95% CI, 0.929-0.960) in dataset A (1205 images, 1
image/patient; 18.1% referable), images adjudicated by panels of Glaucoma
Specialists (GSs); 0.855 (95% CI, 0.841-0.870) in dataset B (9642 images, 1
image/patient; 9.2% referable), images from Atlanta Veterans Affairs Eye Clinic
diabetic teleretinal screening program; and 0.881 (95% CI, 0.838-0.918) in
dataset C (346 images, 1 image/patient; 81.7% referable), images from Dr.
Shroff's Charity Eye Hospital's glaucoma clinic. The algorithm showed
significantly higher sensitivity than 7 of 10 graders not involved in
determining the reference standard, including 2 of 3 GSs, and showed higher
specificity than 3 graders, while remaining comparable to others. For both GSs
and the algorithm, the most crucial features related to referable GON were:
presence of vertical cup-to-disc ratio of 0.7 or more, neuroretinal rim
notching, retinal nerve fiber layer defect, and bared circumlinear vessels. An
algorithm trained on fundus images alone can detect referable GON with higher
sensitivity than and comparable specificity to eye care providers. The
algorithm maintained good performance on an independent dataset with diagnoses
based on a full glaucoma workup.
