Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields

Abstract

PurposeTo validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM–progression of patterns (POP) and VIM-POP). To compare GEM-POP and VIM-POP with other methods.MethodsGEM and VIM models separated cross-sectional abnormal VFs from 859 eyes and normal VFs from 1117 eyes into abnormal and normal clusters. Clusters were decomposed into independent axes. The confidence limit (CL) of stability was established for each axis with a set of 84 stable eyes. Sensitivity for detecting progression was assessed in a sample of 83 eyes with known progressive glaucomatous optic neuropathy (PGON). Eyes were classified as progressed if any defect pattern progressed beyond the CL of stability. Performance of GEM-POP and VIM-POP was compared to point-wise linear regression (PLR), permutation analysis of PLR (PoPLR), and linear regression (LR) of mean deviation (MD), and visual field index (VFI).ResultsSensitivity and specificity for detecting glaucomatous VFs were 89.9% and 93.8%, respectively, for GEM and 93.0% and 97.0%, respectively, for VIM. Receiver operating characteristic (ROC) curve areas for classifying progressed eyes were 0.82 for VIM-POP, 0.86 for GEM-POP, 0.81 for PoPLR, 0.69 for LR of MD, and 0.76 for LR of VFI.ConclusionsGEM-POP was significantly more sensitive to PGON than PoPLR and linear regression of MD and VFI in our sample, while providing localized progression information.Translational RelevanceDetection of glaucomatous progression can be improved by assessing longitudinal changes in localized patterns of glaucomatous defect identified by unsupervised machine learning.

Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects

“…Several studies have employed progression of patterns 13 and unsupervised Gaussian mixture-models 14,15 on VF data. OCT has not been utilized to its full extent when training model machine learning based on OCT information.…”

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“… 42 , 43 In the present study, as we approximately knew the anticipated number of resultant classes for VF test location assignment, 18 , 44 we applied the ISODATA algorithm. As a further point of contrast with the work described by previous authors, which identified clusters of VFs, such as patterns of VF loss, 14 – 17 application of clustering in the present study was to identify clusters of VF spatial test locations that is facilitated by the use of satellite imaging algorithms. Finally, the statistical rigidity of the classification is tested using the D T statistic.…”

“…Other studies have used cluster analysis to identify patterns of VF defects appearing in patients with glaucoma both cross-sectionally 12 , 14 , 41 and to detect progression. 15 – 17 In contrast to studies using mixture of Gaussian models, 15 – 17 k means presents a stricter class assignment for a particular datum point with the assumption that the point is highly certain to belong the assigned class, while a mixture of Gaussian incorporates a degree of uncertainty into class assignment. Their utility has been debated in the literature.…”

“… 12 , 13 Specifically, others have used clustering methods to characterize the location of VF defects in patients with glaucoma, and to apply these clustered maps to then diagnose or detect progression in others. 14 – 17 Notably, these approaches have used the VF results of patients with pre-existing disease to generate clustered maps.…”

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