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Objectives: To evaluate the performance of a deep learning based ArtificialIntelligence (AI) software for detection of glaucoma from stereoscopic opticdisc photographs, and to compare this performance to the performance of a largecohort of ophthalmologists and optometrists.  Methods: A retrospective study evaluating the diagnostic performance of an AIsoftware (Pegasus v1.0, Visulytix Ltd., London UK) and comparing it to that of243 European ophthalmologists and 208 British optometrists, as determined inprevious studies, for the detection of glaucomatous optic neuropathy from 94scanned stereoscopic photographic slides scanned into digital format.  Results: Pegasus was able to detect glaucomatous optic neuropathy with anaccuracy of 83.4% (95% CI: 77.5-89.2). This is comparable to an averageophthalmologist accuracy of 80.5% (95% CI: 67.2-93.8) and average optometristaccuracy of 80% (95% CI: 67-88) on the same images. In addition, the AI systemhad 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. Therewas no statistically significant difference between the performance of the deeplearning system and ophthalmologists or optometrists. There was nostatistically significant difference between the performance of the deeplearning system and ophthalmologists or optometrists.  Conclusion: The AI system obtained a diagnostic performance and repeatabilitycomparable to that of the ophthalmologists and optometrists. We conclude thatdeep learning based AI systems, such as Pegasus, demonstrate significantpromise in the assisted detection of glaucomatous optic neuropathy.

Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study