Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis.
OBJECTIVE
To determine if the patterns uncovered with variational Bayesian-independent component analysis-mixture model (VIM) applied to a large set of normal and glaucomatous fields obtained with the Swedish Interactive Thresholding Algorithm (SITA) are distinct, recognizable, and useful for modeling the severity of the field loss.


METHODS
SITA fields were obtained with the Humphrey Visual Field Analyzer (Carl Zeiss Meditec, Inc, Dublin, California) on 1,146 normal eyes and 939 glaucoma eyes from subjects followed by the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study. VIM modifies independent component analysis (ICA) to develop separate sets of ICA axes in the cluster of normal fields and the 2 clusters of abnormal fields. Of 360 models, the model with the best separation of normal and glaucomatous fields was chosen for creating the maximally independent axes. Grayscale displays of fields generated by VIM on each axis were compared. SITA fields most closely associated with each axis and displayed in grayscale were evaluated for consistency of pattern at all severities.


RESULTS
The best VIM model had 3 clusters. Cluster 1 (1,193) was mostly normal (1,089, 95% specificity) and had 2 axes. Cluster 2 (596) contained mildly abnormal fields (513) and 2 axes; cluster 3 (323) held mostly moderately to severely abnormal fields (322) and 5 axes. Sensitivity for clusters 2 and 3 combined was 88.9%. The VIM-generated field patterns differed from each other and resembled glaucomatous defects (eg, nasal step, arcuate, temporal wedge). SITA fields assigned to an axis resembled each other and the VIM-generated patterns for that axis. Pattern severity increased in the positive direction of each axis by expansion or deepening of the axis pattern.


CONCLUSIONS
VIM worked well on SITA fields, separating them into distinctly different yet recognizable patterns of glaucomatous field defects. The axis and pattern properties make VIM a good candidate as a preliminary process for detecting progression.
Goldbaum
Michael H
MH
Department of Ophthalmology, University of California San Diego, USA.
Jang
Gil-Jin
GJ
Bowd
Chris
C
Hao
Jiucang
J
Zangwill
Linda M
LM
Liebmann
Jeffrey
J
Girkin
Christopher
C
Jung
Tzyy-Ping
TP
Weinreb
Robert N
RN
Sample
Pamela A
PA
D000465:Algorithms; D001499:Bayes Theorem; D003941:Diagnostic Techniques, Ophthalmological / Q000295:instrumentation; D005901:Glaucoma / Q000175:diagnosis / Q000503:physiopathology*; D006801:Humans; D015233:Models, Statistical; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D014794:Visual Fields