Document Detail

Spatial and temporal processing of threshold data for detection of progressive glaucomatous visual field loss.
MedLine Citation:
PMID:  11831919     Owner:  NLM     Status:  MEDLINE    
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. CLINICAL RELEVANCE: 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.
Paul G D Spry; Chris A Johnson; Alex B Bates; Andrew Turpin; Balwantray C Chauhan
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Archives of ophthalmology     Volume:  120     ISSN:  0003-9950     ISO Abbreviation:  Arch. Ophthalmol.     Publication Date:  2002 Feb 
Date Detail:
Created Date:  2002-02-08     Completed Date:  2002-02-25     Revised Date:  2007-11-14    
Medline Journal Info:
Nlm Unique ID:  7706534     Medline TA:  Arch Ophthalmol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  173-80     Citation Subset:  AIM; IM    
Bristol Eye Hospital, Lower Maudlin Street, Bristol BS1 2LX, England.
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MeSH Terms
Computer Simulation
Disease Progression
Glaucoma / diagnosis*
Linear Models
Models, Biological
Perimetry / methods*
Sensitivity and Specificity
Sensory Thresholds
Vision Disorders / diagnosis*
Visual Fields*
Grant Support

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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