Artificial intelligence may improve glaucoma management
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This study used an unsupervised artificial intelligence (AI) model to quantify central visual field (VF) loss patterns in eyes with glaucoma.

In this multicenter retrospective study, researchers performed a cross-sectional analysis of the central VF patterns of 13,951 Humphrey 10-2 visual field tests using total deviation plots by archetypal analysis—an unsupervised AI method—in eyes with all glaucoma severities.

Analysis identified 17 distinct central VF patterns; these patterns could be divided into isolated superior loss, isolated inferior loss, diffuse loss, and other loss patterns. Within the diffuse loss pattern, 4 of the 5 patterns preserved the less vulnerable inferotemporal zone, whereas they lost most of the remaining more vulnerable zones. Features predicting a more negative 10-2 MD slope were identified as older age, decreased MD and pattern standard deviation of baseline VF. Inclusion of coefficients from central VF archetypal patterns improved the prediction of central VF MD slope.

Use of AI has the potential to improve glaucoma diagnosis and prognosis, especially unsupervised AI, which can help clinicians track patterns of glaucomatous damage and changes over time. This study confirmed previous observational findings of arcuate defects and less-vulnerable zones in the central VF. The ability to trace and detect focal changes of central VF over time can be used to develop new progression detection algorithms, which can better identify potential quality-of-life impairment in glaucoma patients than the use of central VF MD alone.