A Case for the Use of Artificial Intelligence in Glaucoma Assessment.

We hypothesize that artificial intelligence (AI) applied to relevant clinical testing in glaucoma has the potential to enhance the ability to detect glaucoma. This premise was discussed at the recent Collaborative Community on Ophthalmic Imaging meeting, "The Future of Artificial Intelligence-Enabled Ophthalmic Image Interpretation: Accelerating Innovation and Implementation Pathways," held virtually September 3-4, 2020. The Collaborative Community on Ophthalmic Imaging (CCOI) is an independent self-governing consortium of stakeholders with broad international representation from academic institutions, government agencies, and the private sector whose mission is to act as a forum for the purpose of helping speed innovation in healthcare technology. It was 1 of the first 2 such organizations officially designated by the Food and Drug Administration in September 2019 in response to their announcement of the collaborative community program as a strategic priority for 2018-2020. Further information on the CCOI can be found online at their website (https://www.cc-oi.org/about). Artificial intelligence for glaucoma diagnosis would have high utility globally, because access to care is limited in many parts of the world and half of all people with glaucoma are unaware of their illness. The application of AI technology to glaucoma diagnosis has the potential to broadly increase access to care worldwide, in essence flattening the Earth by providing expert-level evaluation to individuals even in the most remote regions of the planet.

Can Macula and Optic Nerve Head Parameters Detect Glaucoma Progression in Eyes with Advanced Circumpapillary Retinal Nerve Fiber Layer Damage?

PURPOSE: To evaluate the ability of OCT optic nerve head (ONH) and macular parameters to detect disease progression in eyes with advanced structural glaucomatous damage of the circumpapillary retinal nerve fiber layer (cRNFL). DESIGN: Longitudinal study. PARTICIPANTS: Forty-four eyes from 37 patients with advanced average cRNFL damage (≤60 µm) followed up for an average of 4.0 years. METHODS: All patients were examined with spectral-domain OCT and visual field (VF) assessment during at least 4 visits. MAIN OUTCOME MEASUREMENTS: Visual field mean deviation (MD) and VF index. OCT cRNFL (average, superior, and inferior quadrants), ganglion cell-inner plexiform layer (GCIPL) (average, superior, and inferior), rim area, cup volume, average cup-to-disc (C:D) ratio, and vertical C:D ratio. RESULTS: At baseline, patients had a median VF MD of -10.18 dB and mean cRNFL of 54.55±3.42 µm. The rate of change for MD and VF index were significant. No significant rate of change was noted for cRNFL, whereas significant (P < 0.001) rates were detected for GCIPL (-0.57±0.05 µm/year) and ONH parameters such as rim area (-0.010±0.001 mm2/year). CONCLUSIONS: Macula GCIPL and ONH parameters may be useful in tracking progression in patients with advanced glaucoma.

Glaucoma Diagnosis: from the Artisanal to the Defined.

In 400BC, Hippocrates wrote the first record of glaucoma. Since then, increasingly objective diagnostic techniques have enabled earlier detection of glaucoma and its progression, providing greater certainty in decision-making and early medical and surgical intervention.