Progression of patterns (POP): a machine classifier algorithm to identify glaucoma progression in visual fields.
OBJECTIVE
We evaluated Progression of Patterns (POP) for its ability to identify progression of glaucomatous visual field (VF) defects.


METHODS
POP uses variational Bayesian independent component mixture model (VIM), a machine learning classifier (MLC) developed previously. VIM separated Swedish Interactive Thresholding Algorithm (SITA) VFs from a set of 2,085 normal and glaucomatous eyes into nine axes (VF patterns): seven glaucomatous. Stable glaucoma was simulated in a second set of 55 patient eyes with five VFs each, collected within four weeks. A third set of 628 eyes with 4,186 VFs (mean ± SD of 6.7 ± 1.7 VFs over 4.0 ± 1.4 years) was tested for progression. Tested eyes were placed into suspect and glaucoma categories at baseline, based on VFs and disk stereoscopic photographs; a subset of eyes had stereophotographic evidence of progressive glaucomatous optic neuropathy (PGON). Each sequence of fields was projected along seven VIM glaucoma axes. Linear regression (LR) slopes generated from projections onto each axis yielded a degree of confidence (DOC) that there was progression. At 95% specificity, progression cutoffs were established for POP, visual field index (VFI), and mean deviation (MD). Guided progression analysis (GPA) was also compared.


RESULTS
POP identified a statistically similar number of eyes (P > 0.05) as progressing compared with VFI, MD, and GPA in suspects (3.8%, 2.7%, 5.6%, and 2.9%, respectively), and more eyes than GPA (P = 0.01) in glaucoma (16.0%, 15.3%, 12.0%, and 7.3%, respectively), and more eyes than GPA (P = 0.05) in PGON eyes (26.3%, 23.7%, 27.6%, and 14.5%, respectively).


CONCLUSIONS
POP, with its display of DOC of progression and its identification of progressing VF defect pattern, adds to the information available to the clinician for detecting VF progression.
Hamilton Glaucoma Center, Department of Ophthalmology, University of California at San Diego, La Jolla, CA 92093, USA. mgoldbaum@ucsd.edu
Michael H
MH
Goldbaum
Intae
I
Lee
Giljin
G
Jang
Madhusudhanan
M
Balasubramanian
Pamela A
PA
Sample
Robert N
RN
Weinreb
Jeffrey M
JM
Liebmann
Christopher A
CA
Girkin
Douglas R
DR
Anderson
Linda M
LM
Zangwill
Marie-Josee
MJ
Fredette
Tzyy-Ping
TP
Jung
Felipe A
FA
Medeiros
Christopher
C
Bowd
D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence / Q000145:classification*; D018450:Disease Progression; D005901:Glaucoma / Q000175:diagnosis*; D006068:Gonioscopy; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007429:Intraocular Pressure / Q000502:physiology; D008875:Middle Aged; D009412:Nerve Fibers / Q000473:pathology; D009898:Optic Disk / Q000473:pathology; D009901:Optic Nerve Diseases / Q000175:diagnosis*; D012165:Retinal Ganglion Cells / Q000473:pathology; D014786:Vision Disorders / Q000175:diagnosis*; D014792:Visual Acuity / Q000502:physiology; D058609:Visual Field Tests / Q000145:classification*; D014794:Visual Fields