Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers.
OBJECTIVE
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.
Hamilton Glaucoma Center, Department of Ophthalmology, University of California, San Diego, La Jolla, California, United States of America.
Christopher
C
Bowd
Hamilton Glaucoma Center, Department of Ophthalmology, University of California, San Diego, La Jolla, California, United States of America.
Robert N
RN
Weinreb
Hamilton Glaucoma Center, Department of Ophthalmology, University of California, San Diego, La Jolla, California, United States of America.
Madhusudhanan
M
Balasubramanian
Hamilton Glaucoma Center, Department of Ophthalmology, University of California, San Diego, La Jolla, California, United States of America.
Intae
I
Lee
Hamilton Glaucoma Center, Department of Ophthalmology, University of California, San Diego, La Jolla, California, United States of America ; School of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea.
Giljin
G
Jang
Hamilton Glaucoma Center, Department of Ophthalmology, University of California, San Diego, La Jolla, California, United States of America.
Siamak
S
Yousefi
Hamilton Glaucoma Center, Department of Ophthalmology, University of California, San Diego, La Jolla, California, United States of America.
Linda M
LM
Zangwill
Hamilton Glaucoma Center, Department of Ophthalmology, University of California, San Diego, La Jolla, California, United States of America.
Felipe A
FA
Medeiros
Department of Ophthalmology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
Christopher A
CA
Girkin
Department of Ophthalmology, New York University School of Medicine, New York, New York, United States of America ; New York Eye and Ear Infirmary, New York, New York, United States of America.
Jeffrey M
JM
Liebmann
Hamilton Glaucoma Center, Department of Ophthalmology, University of California, San Diego, La Jolla, California, United States of America.
Michael H
MH
Goldbaum
D000328:Adult; D000368:Aged; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016022:Case-Control Studies; D016000:Cluster Analysis; D003198:Computer Simulation; D005901:Glaucoma / Q000175:diagnosis*; D006801:Humans; D008875:Middle Aged; D008954:Models, Biological; D012680:Sensitivity and Specificity