Bex Peter J

An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis.

The archetype method can inform clinicians of VF progression patterns.

Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma.

We quantified central VF patterns in glaucoma, which were used to improve the prediction of central VF worsening compared with using only global indices.

Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence.

In this study, central VF loss in end-stage glaucoma was found to exhibit characteristic patterns that might be associated with different subtypes.

Reversal of Glaucoma Hemifield Test Results and Visual Field Features in Glaucoma.

Using VF features may predict the GHT results reversal to WNL after 2 consecutive ONL results.

An Artificial Intelligence Approach to Assess Spatial Patterns of Retinal Nerve Fiber Layer Thickness Maps in Glaucoma.

Using RPs improved the VF prediction compared with using sectoral RNFLTs.

Relationship Between Central Retinal Vessel Trunk Location and Visual Field Loss in Glaucoma.

CRVTL nasalization is significantly and exclusively correlated to central VF loss for all glaucoma severities independent of cpRNFLT, and thus might be a structural biomarker of central VF loss.

Predicting Global Test-Retest Variability of Visual Fields in Glaucoma.

Inclusion of archetype VF loss patterns and TD values based on first VF improved the prediction of the global test-retest variability than using traditional global VF indices alone.

Inter-Eye Association of Visual Field Defects in Glaucoma and Its Clinical Utility.

VF patterns of the worse eye are predictive of VF defects in the better eye.

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