Recognizing patterns of visual field loss using unsupervised machine learning

Abstract

Glaucoma is a potentially blinding optic neuropathy that results in a decrease in visual sensitivity. Visual field abnormalities (decreased visual sensitivity on psychophysical tests) are the primary means of glaucoma diagnosis. One form of visual field testing is Frequency Doubling Technology (FDT) that tests sensitivity at 52 points within the visual field. Like other psychophysical tests used in clinical practice, FDT results yield specific patterns of defect indicative of the disease. We used Gaussian Mixture Model with Expectation Maximization (GEM), (EM is used to estimate the model parameters) to automatically separate FDT data into clusters of normal and abnormal eyes. Principal component analysis (PCA) was used to decompose each cluster into different axes (patterns). FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal (i.e., glaucomatous) FDT results, recruited from a university-based, longitudinal, multi-center, clinical study on glaucoma. The GEM input was the 52-point FDT threshold sensitivities for all eyes. The optimal GEM model separated the FDT fields into 3 clusters. Cluster 1 contained 94% normal fields (94% specificity) and clusters 2 and 3 combined, contained 77% abnormal fields (77% sensitivity). For clusters 1, 2 and 3 the optimal number of PCA-identified axes were 2, 2 and 5, respectively. GEM with PCA successfully separated FDT fields from healthy and glaucoma eyes and identified familiar glaucomatous patterns of loss.

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

“…We first evaluate the ability of GEM to cluster healthy and glaucomatous VFs and to generate patterns of visual field defects within each cluster. 28 , 31 We then compare the clustering performance of GEM with VIM, based on specificity and sensitivity for clustering VFs as healthy and glaucomatous. Next, we detect glaucomatous progression in study eyes based on significant change of longitudinal VF measurements (exams) along the previously generated GEM and VIM defect patterns, using POP.…”

“…GEM has been described in detail in a previous publication. 28 Briefly, GEM combines multivariate Gaussian components to model the VF data points and uses the expectation maximization (EM) procedure to estimate the parameters of the model, iteratively. Similar to VIM, we used absolute sensitivity at 52 SAP locations and participant age as inputs to GEM.…”

“…The estimation of the best structure representation was accomplished with post hoc assessment of the MD of the clusters, and visual inspection of the patterns of defect within the observed clusters. 24 – 28 We aimed to diminish the effects of human bias by designing a process for detecting change over time along mathematically determined glaucomatous patterns obtained by unsupervised learning techniques without human intervention, and we aimed to improve effectiveness by eliminating noncontributing data and concentrating on the data that are changing. 29 – 31 …”

“… 24 , 27 , 32 The Gaussian mixture-model with expectation maximization (GEM) produces a similar output, but learns 50 times faster and is a fully automated unsupervised learning approach. 28 , 31 The application of progression of patterns (POP) to VIM and GEM make the progression detectors VIM-POP and GEM-POP. 29 – 31 …”

Detecting glaucomatous change in visual fields: Analysis with an optimization framework

“…6) Variational Bayesian independent component analysis mixture model (VIM) for glaucoma defect pattern identification and progression detection is a machine learning classification-based approach developed by our group [16–18]. 7) Gaussian Mixture Model with Expectation Maximization (GEM), another machine learning-based approach, recently was introduced by our group and was successfully applied to visual field data to identify glaucoma-related defect patterns and to detect progression [19]. The initial creation of an environment using these machine learning approaches for progression detection is computationally and algorithmically complex.…”