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Proceedings Paper

Recognizing patterns of visual field loss using unsupervised machine learning
Author(s): Siamak Yousefi; Michael H. Goldbaum; Linda M. Zangwill; Felipe A. Medeiros; Christopher Bowd
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Paper 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.

Paper Details

Date Published: 21 March 2014
PDF: 10 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90342M (21 March 2014); doi: 10.1117/12.2043145
Show Author Affiliations
Siamak Yousefi, Univ. of California, San Diego (United States)
Michael H. Goldbaum, Univ. of California, San Diego (United States)
Linda M. Zangwill, Univ. of California, San Diego (United States)
Felipe A. Medeiros, Univ. of California, San Diego (United States)
Christopher Bowd, Univ. of California, San Diego (United States)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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