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.

Glaucoma, a group of diseases characterized by progressive optic nerve degeneration that results in irreversible blindness, can be considered a neurodegenerative disorder of both the eye and the brain. Increasing evidence from human and animal studies have shown that glaucoma shares some common neurodegenerative pathways with Alzheimer's disease (AD) and other tauopathies, such as chronic traumatic encephalopathy (CTE) and frontotemporal dementia. This hypothesis is based on the focal adhesion pathway hypothesis and the spreading hypothesis of tau. Not only has the Apolipoprotein E (APOE) gene been shown to be associated with AD, but also with primary open angle glaucoma (POAG). This review will highlight the relevant literature in the past 20 years from PubMed that show the pathogenic overlap between POAG and AD. Neurodegenerative pathways that contribute to transsynaptic neurodegeneration in AD and other tauopathies might also be similar to those in glaucomatous neurodegeneration.

It is crucial for neuro-ophthalmologists to be updated and familiar with these newer OCT imaging protocols and to make appropriate choices for different clinical scenarios, in order to optimize the diagnostic sensitivity and specificity.