Macula Spatial Patterns and their Association with Central Visual Field Progression in Glaucoma using Artificial Intelligence.

Purpose

To provide spatial patterns of ganglion cell complex thickness and assess their associations with central visual field progression in glaucoma.

Methods

Macular patterns from the ganglion cell complex were determined using an artificial intelligence algorithm termed archetypal analysis (AA). The diagnostic accuracy of spatial patterns for detecting 10-2 central visual field progression in eyes with a minimum of five 10-2 visual field tests was calculated and compared with the mean global ganglion cell complex thickness. Eyes with progression on either of two trend-based methods (significant MD slope <-0.5 dB/year or clustered pointwise linear regression) were classified as 'progressors'.

Results

A total of 4031 macular scans of 1093 eyes (611 patients) were included, with a mean (SD) age of 67.8 (12.7) years. Eleven distinct spatial patterns were identified. While the macular vulnerable zone was preferentially affected in four patterns, most of the less vulnerable zones were preserved. The AA models at baseline achieved AUROC (0.73 [95% CI 0.62-0.84]) and outperformed global ganglion cell complex thickness (0.55 [95% CI 0.46-0.61], P=0.01) for predicting central VF progression in eyes with early disease at baseline. The AA models AUROC (0.70 [95% CI 0.59-0.80]) also outperformed ganglion cell complex thickness (0.55 [95% CI 0.48-0.60], P=0.02) for predicting central VF progression across all severities.

Conclusions

Using unsupervised artificial intelligence, characteristic patterns of macular thinning were identified and associated with central visual field progression. Spatial macular pattern analysis may enhance individualized care and improve risk stratification for those at risk of central VF damage.