  Objectives: To evaluate the performance of a deep learning based Artificial
Intelligence (AI) software for detection of glaucoma from stereoscopic optic
disc photographs, and to compare this performance to the performance of a large
cohort of ophthalmologists and optometrists.
  Methods: A retrospective study evaluating the diagnostic performance of an AI
software (Pegasus v1.0, Visulytix Ltd., London UK) and comparing it to that of
243 European ophthalmologists and 208 British optometrists, as determined in
previous studies, for the detection of glaucomatous optic neuropathy from 94
scanned stereoscopic photographic slides scanned into digital format.
  Results: Pegasus was able to detect glaucomatous optic neuropathy with an
accuracy of 83.4% (95% CI: 77.5-89.2). This is comparable to an average
ophthalmologist accuracy of 80.5% (95% CI: 67.2-93.8) and average optometrist
accuracy of 80% (95% CI: 67-88) on the same images. In addition, the AI system
had an intra-observer agreement (Cohen's Kappa, $\kappa$) of 0.74 (95% CI:
0.63-0.85), compared to 0.70 (range: -0.13-1.00; 95% CI: 0.67-0.73) and 0.71
(range: 0.08-1.00) for ophthalmologists and optometrists, respectively. There
was no statistically significant difference between the performance of the deep
learning system and ophthalmologists or optometrists. There was no
statistically significant difference between the performance of the deep
learning system and ophthalmologists or optometrists.
  Conclusion: The AI system obtained a diagnostic performance and repeatability
comparable to that of the ophthalmologists and optometrists. We conclude that
deep learning based AI systems, such as Pegasus, demonstrate significant
promise in the assisted detection of glaucomatous optic neuropathy.
