Fuzzy clustering of 24–2 visual field patterns can detect glaucoma progression

Purpose: To represent 24–2 visual field (VF) losses of individual patients using a hybrid approach of archetypal analysis (AA) and fuzzy c-means (FCM) clustering. Methods: In this multicenter retrospective study, we classified characteristic patterns of 24–2 VF using AA and decomposed them with FCM clustering. We predicted the change in mean deviation (MD) through supervised machine learning from decomposition coefficient change. In addition, we compared the areas under the receiver operating characteristic curves (AUCs) of the decomposition coefficient slopes to detect VF progression using three criteria: MD slope, Visual Field Index slope, and pointwise linear regression analysis. Results: We identified 16 characteristic patterns (archetypes or ATs) of 24–2 VF from 132,938 VFs of 18,033 participants using AA. The hybrid approach using FCM revealed a lower mean squared error and greater correlation coefficient than the AA single approach for predicting MD change (all P ≤ 0.001). Three of 16 AUCs of the FCM decomposition coefficient slopes outperformed the AA decomposition coefficient slopes in detecting VF progression for all three criteria (AT5, superior altitudinal defect; AT10, double arcuate defect; AT13, total loss) (all P ≤ 0.028). Conclusion: A hybrid approach combining AA and FCM to analyze 24–2 VF can visualize VF tests in characteristic patterns and enhance detection of VF progression with lossless decomposition.