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Almut Bindewald, Alan C. Bird, Samantha S. Dandekar, Joanna Dolar-Szczasny, Jens Dreyhaupt, Frederick W. Fitzke, Wilma Einbock, Frank G. Holz, Jork J. Jorzik, Claudia Keilhauer, Noemi Lois, Juliane Mlynski, Daniel Pauleikhoff, Giovanni Staurenghi, Sebastian Wolf; Classification of Fundus Autofluorescence Patterns in Early Age-Related Macular Disease. Invest. Ophthalmol. Vis. Sci. 2005;46(9):3309-3314. doi: 10.1167/iovs.04-0430.
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purpose. To describe and classify patterns of abnormal fundus autofluorescence (FAF) in eyes with early nonexudative age-related macular disease (AMD).
methods. FAF images were recorded in eyes with early AMD by confocal scanning laser ophthalmoscopy (cSLO) with excitation at 488 nm (argon or OPSL laser) and emission above 500 or 521 nm (barrier filter). A standardized protocol for image acquisition and generation of mean images after automated alignment was applied, and routine fundus photographs were obtained. FAF images were classified by two independent observers. The κ statistic was applied to assess intra- and interobserver variability.
results. Alterations in FAF were classified into eight phenotypic patterns including normal, minimal change, focal increased, patchy, linear, lacelike, reticular, and speckled. Areas with abnormal increased or decreased FAF signals may or may not have corresponded to funduscopically visible alterations. For intraobserver variability, κ of observer I was 0.80 (95% confidence interval [CI]0.71–0.89) and of observer II, 0.74. (95% CI, 0.64–0.84). For interobserver variability, κ was 0.77 (95% CI, 0.67–0.87).
conclusions. Various phenotypic patterns of abnormal FAF can be identified with cSLO imaging. Distinct patterns may reflect heterogeneity at a cellular and molecular level in contrast to a nonspecific aging process. The results indicate that the classification system yields a relatively high degree of intra- and interobserver agreement. It may be applicable for determination of novel prognostic determinants in longitudinal natural history studies, for identification of genetic risk factors, and for monitoring of future therapeutic interventions to slow the progression of early AMD.
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