and for the BONSAI (Brain and Optic Nerve Study with Artificial Intelligence) Study Group Objective: To compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro-ophthalmologists in classifying optic disc appearance. Methods: The deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro-ophthalmologists who independently reviewed the same randomly presented images without clinical information. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated. Results: The system correctly classified 678 of 800 (84.7%) photographs, compared with 675 of 800 (84.4%) for Expert 1 and 641 of 800 (80.1%) for Expert 2. The system yielded areas under the receiver operating characteristic curve of 0.97 (95% confidence interval [CI] = 0.96-0.98), 0.96 (95% CI = 0.94-0.97), and 0.89 (95% CI = 0.87-0.92) for the detection of normal discs, papilledema, and other disc abnormalities, respectively. The accuracy, sensitivity, and specificity of the system's classification of optic discs were similar to or better than the 2 experts. Intergrader agreement at the eye level was 0.71 (95% CI = 0.67-0.76) between Expert 1 and Expert 2, 0.72 (95% CI = 0.68-0.76) between the system and Expert 1, and 0.65 (95% CI = 0.61-0.70) between the system and Expert 2. Interpretation: The performance of this deep learning system at classifying optic disc abnormalities was at least as good as 2 expert neuro-ophthalmologists. Future prospective studies are needed to validate this system as a diagnostic aid in relevant clinical settings.

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Background: Optic neuritis (ON) is often the presenting symptom in inflammatory central nervous system demyelinating disorders. Objective: To compare the frequency and pattern of optic chiasm involvement in patients with aquaporin-4-immunoglobulin G (AQP4-IgG)-associated ON to patients with myelin oligodendrocyte glycoprotein-immunoglobulin G (MOG-IgG)-associated ON. Methods: Retrospective review of all patients evaluated at Mayo Clinic, Stanford University and Ramathibodi Hospital who were found to have: (1) ON, (2) either MOG-IgG or AQP4-IgG by cell-based assay, and (3) magnetic resonance imaging (MRI) at the time of ON. MRI was reviewed for contrast enhancement of the optic chiasm and the pattern of involvement. Results: One hundred and fifty-four patients (74 AQP4-IgG and 80 MOG-IgG) were included. Among patients with AQP4-IgG-ON, 20% had chiasmal involvement, compared with 16% of patients with MOG-IgG-ON ( p = 0.66). In patients with chiasmal involvement, longitudinally extensive optic nerve enhancement (from orbit extending to chiasm) was identified in 54% of MOG-IgG-ON patients, compared with 7% of AQP4-IgG-ON patients ( p = 0.01). Conclusion: Chiasmal involvement of MOG-IgG-ON and AQP4-IgG-ON occur at more similar frequencies than previously reported. Furthermore, MOG-IgG-ON chiasmal involvement is more likely to be part of a longitudinally extensive optic nerve lesion.