Spatial and temporal processing of threshold data for detection of progressive glaucomatous visual field loss.
OBJECTIVE
To evaluate the effect of spatial and temporal filtering of threshold visual field data on the ability of pointwise linear regression (PLR) to detect progressive glaucomatous visual field loss.


METHODS
Longitudinal visual field data (Full-Threshold Program 30-2 test point pattern) were simulated using a computer model of glaucomatous visual field progression. This approach permitted construction of a "gold standard" because matching visual field data without variability could be generated and analyzed. Four clustered progressive defects were produced, consisting of 2, 3, 9, and 18 locations, respectively, each with progression rates of -1 and -2.5 dB/y. Pointwise linear regression was used to identify progressive test locations (criterion for progression of statistically significant slope of < or =-1 dB/y, P<.05). Each visual field series was analyzed after the following 3 procedures: (1) no filtering (unprocessed data), (2) Gaussian spatial possessing (3 x 3 grid), and (3) temporal processing (2 field moving average). The effect of spatial and temporal processing on PLR discriminatory power for progression detection was quantified by comparison with the gold standard.


RESULTS
Spatial processing reduced PLR sensitivity to levels below that achieved for analysis of unprocessed data for small progressive defects (< or =9 locations) or at the low true progression rate (-1 dB/y). Under these conditions, spatial processing caused small PLR specificity improvement. Spatial processing only improved PLR sensitivity above unprocessed levels when progressive defects were large and changing rapidly (progression rate of -2.5 dB/y). Temporal processing gave consistent PLR improvement in sensitivity for all defect sizes and true progression rates. Pointwise linear regression sensitivity gain provided by temporal processing allowed progression to be detected 2 to 3 visual fields earlier than for analysis of raw data. Specificity dropped slightly as a result of temporal processing but remained at 89% or above for all conditions studied.


CONCLUSIONS
Gaussian spatial processing reduces PLR discriminatory power with low true progression rates or small progressive defect sizes and, therefore, is of limited use for detection of progressive visual field loss. Temporal processing improves the sensitivity of PLR and reduces the number of tests required to detect progressive loss with minimal loss of specificity.


CONCLUSIONS
Image processing techniques can be applied to threshold visual field data to enhance sensitivity or specificity of PLR for the determination of progressive change. This investigation demonstrates that temporal processing may assist with the detection of significant progressive visual field loss with fewer test results than unprocessed data.
Spry
Paul G D
PG
Bristol Eye Hospital, Lower Maudlin Street, Bristol BS1 2LX, England. paul.spry@ubht.swest.nhs.uk
Johnson
Chris A
CA
Bates
Alex B
AB
Turpin
Andrew
A
Chauhan
Balwantray C
BC
D003198:Computer Simulation; D018450:Disease Progression; D005901:Glaucoma / Q000175:diagnosis*; D006801:Humans; D016014:Linear Models; D008954:Models, Biological; D012680:Sensitivity and Specificity; D012684:Sensory Thresholds; D014786:Vision Disorders / Q000175:diagnosis*; D058609:Visual Field Tests / Q000379:methods*; D014794:Visual Fields