Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers

Abstract

Purpose: The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes representing statistically independent patterns of defect in the glaucoma clusters.

Methods: FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal FDT results from the UCSD-based Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES). For all eyes, VIM input was 52 threshold test points from the 24-2 test pattern, plus age.

Results: FDT mean deviation was -1.00 dB (S.D. = 2.80 dB) and -5.57 dB (S.D. = 5.09 dB) in FDT-normal eyes and FDT-abnormal eyes, respectively (p<0.001). VIM identified meaningful clusters of FDT data and positioned a set of statistically independent axes through the mean of each cluster. The optimal VIM model separated the FDT fields into 3 clusters. Cluster N contained primarily normal fields (1109/1190, specificity 93.1%) and clusters G1 and G2 combined, contained primarily abnormal fields (651/786, sensitivity 82.8%). For clusters G1 and G2 the optimal number of axes were 2 and 5, respectively. Patterns automatically generated along axes within the glaucoma clusters were similar to those known to be indicative of glaucoma. Fields located farther from the normal mean on each glaucoma axis showed increasing field defect severity.

Conclusions: VIM successfully separated FDT fields from healthy and glaucoma eyes without a priori information about class membership, and identified familiar glaucomatous patterns of loss.

Figure 1. Scatter plot showing sensitivity (Y) and specificity (X) of each of 720 variational Bayesian independent component analysis mixture (VIM) models created from FDT Matrix threshold sensitivities (52 inputs, plus age).

Results for the two best models (defined subjectively with a goal of 0.90 specificity and a maximum sensitivity) are shown.

Figure 2. Plot of axis contribution of variational Bayesian independent component analysis mixture (VIM) (Y) versus number of axes (X).

Axes beyond the knee point were removed, leaving 2 axes each for Clusters 1 and 2 and 5 axes for Cluster 3. The best VIM model was retrained 500 times, constrained to the reduced number of axes.

Figure 3. Color-coded displays simulating total deviation plots along with age at −2 and +2 standard deviations of each axis from the centroid of Cluster N, that was composed primarily of normal FDT fields.

Axis 1 and Axis 2 appear normal or near normal. Numerical values shown are simulated total deviation values at each corresponding test point.

Figure 4. Color-coded displays simulating total deviation plots along with age at −2 and +2 standard deviations of each axis from the centroid of the normal Cluster N, that was composed primarily of abnormal FDT fields.

Axis 1 appears to represent primarily moderate superior hemifield defects and Axis 2 appears to represent primarily moderate inferior hemifield defects (i.e., both are altitudinal defects), both showing less severe, diffuse loss in the opposing hemifield.

Figure 5. Color-coded displays simulating total deviation plots along with age at −2 and +2 standard deviations of each axis from the centroid of normal Cluster N, that was composed entirely of abnormal FDT fields.

Axis 1 appears to represent diffuse moderate visual field loss. Axis 2 and Axis 3 appear to represent more severe superior nasal and inferior nasal defects, respectively. Axis 4 appears to represent combined superior and inferior nasal step defects, and Axis5 appears to represent a diffuse pattern of loss, primarily localized superiorly.