Distinguishing wet from dry age-related macular degeneration using three-dimensional computer-automated threshold Amsler grid testing

Craig D. Robison, Renu V. Jivrajka, Simon R. Bababeygy, Wolfgang Fink, Alfredo A. Sadun, J. Sebag

Research output: Contribution to journalArticle

20 Scopus citations


Background/aims: With the increased efficacy of current therapy for wet age-related macular degeneration (AMD), better ways to detect wet AMD are needed. This study was designed to test the ability of threedimensional contrast threshold Amsler grid (3D-CTAG) testing to distinguish wet AMD from dry AMD. Methods: Conventional paper Amsler grid and 3D-CTAG tests were performed in 90 eyes: 63 with AMD (34 dry, 29 wet) and 27 controls. Qualitative comparisons were based upon the three-dimensional shapes of central visual field (VF) defects. Quantitative analyses considered the number and volume of the three-dimensional defects. Results: 25/34 (74%) dry AMD and 6/29 (21%) wet AMD eyes had no distortions on paper Amsler grid. Of these, 5/25 (20%) dry and 6/6 (100%) wet (p=0.03) AMD eyes exhibited central VF defects with 3D-CTAG. Wet AMD displayed stepped defects in 16/28 (57%) eyes, compared with only 2/34 (6%) of dry AMD eyes (p=0.002). All three volumetric indices of VF defects were two- to four-fold greater in wet than dry AMD (p<0.006). 3D-CTAG had 83.9% positive and 90.6% negative predictive values for wet AMD. Conclusions: 3D-CTAG has a higher likelihood of detecting central VF defects than conventional Amsler grid, especially in wet AMD. Wet AMD can be distinguished from dry AMD by qualitative and quantitative 3D-CTAG criteria. Thus, 3D-CTAG may be useful in screening for wet AMD, quantitating disease severity, and providing a quantitative outcome measure of therapy.

Original languageEnglish (US)
Pages (from-to)1419-1423
Number of pages5
JournalBritish Journal of Ophthalmology
Issue number10
Publication statusPublished - Oct 2011


ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems
  • Cellular and Molecular Neuroscience

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