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Macula Spatial Patterns and Their Association With Central Visual Field Progression in Glaucoma Using Artificial

Golnoush Mahmoudinezhad1, Sasan Moghimi1, Varun Pawar2

  • 1Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology.

Journal of Glaucoma
|March 24, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence identified distinct spatial patterns of ganglion cell complex thinning. These patterns predict central visual field progression in glaucoma patients more accurately than global thickness measurements.

Keywords:
artificial intelligenceglaucomamaculaoptical coherence tomographypattern

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence in Medicine
  • Glaucoma Research

Background:

  • Glaucoma is a leading cause of irreversible blindness.
  • Early detection of visual field progression is crucial for managing glaucoma.
  • Current methods for assessing glaucoma progression have limitations.

Purpose of the Study:

  • To identify spatial patterns of ganglion cell complex (GCC) thickness using artificial intelligence.
  • To assess the association between these spatial GCC patterns and central visual field (VF) progression in glaucoma.
  • To compare the diagnostic accuracy of spatial GCC patterns with global GCC thickness for predicting VF progression.

Main Methods:

  • Archetypal analysis (AA), an AI algorithm, was used to determine macular GCC patterns.
  • Diagnostic accuracy was calculated for spatial patterns in predicting 10-2 central VF progression.
  • Eyes with at least five 10-2 VF tests were included, and progression was defined by trend-based methods.

Main Results:

  • Eleven distinct spatial GCC patterns were identified from 4031 macular scans.
  • AA models outperformed global GCC thickness in predicting central VF progression (AUROC 0.73 vs. 0.55 at baseline, P=0.01).
  • AA models also showed superior prediction across all glaucoma severities (AUROC 0.70 vs. 0.55, P=0.02).

Conclusions:

  • Unsupervised AI successfully identified characteristic macular thinning patterns linked to central VF progression in glaucoma.
  • Spatial macular pattern analysis shows promise for enhancing individualized glaucoma care.
  • This approach may improve risk stratification for patients susceptible to central VF damage.