Journal of glaucoma · 2026
Macula Spatial Patterns and Their Association With Central Visual Field Progression in Glaucoma Using Artificial Intelligence.
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Mahmoudinezhad Golnoush, Moghimi Sasan, Pawar Varun, Cheng Jiacheng, Walker Evan, Beheshtaein Siavash, Yong Yu Xuan, Beheshtaein Soroosh, Alam Naimul A, Parupudi V S Raghu, Meller Leo, Nishida Takashi, Liebmann Jeffrey M, Girkin Christopher A, Fazio Massimo A, Christopher Mark, Zangwill Linda M, Vasconcelos Nuno, Weinreb Robert N
Аннотация
PRECIS: Artificial intelligence-derived macular thinning patterns were associated with central visual field progression in glaucoma and outperformed global thickness metrics in predicting progression across disease severities.
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 at least five 10-2 visual field tests was calculated and compared with the mean global ganglion cell complex thickness. Eyes with progression on either of 2 trend-based methods (significant MD slope less than -0.5 dB/y 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 4 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 progression in the central visual field. Spatial macular pattern analysis may enhance individualized care and improve risk stratification for those at risk of central VF damage.
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 at least five 10-2 visual field tests was calculated and compared with the mean global ganglion cell complex thickness. Eyes with progression on either of 2 trend-based methods (significant MD slope less than -0.5 dB/y 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 4 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 progression in the central visual field. Spatial macular pattern analysis may enhance individualized care and improve risk stratification for those at risk of central VF damage.