Accurate assessment of disease burden and determination of disease progression are challenging in dry age-related macular degeneration (AMD). We assessed the utility of quantified fundus autofluorescence in (FAF) the evaluation and follow-up of dry AMD.
To develop a method for quantitative FAF image analysis that is capable of stratifying severity of nonexudative AMD.
Design, Setting, and Participants
A retrospective analysis from 2008 to 2012 at a university eye center of FAF images taken of normal and nonexudative AMD eyes compared the Index of Retinal Autofluorescence (IRA) with retinal specialists’ clinical rankings of FAF images and the Age-Related Eye Disease Study (AREDS) grading scheme of corresponding color fundus photographs.
Digital files of Heidelberg Spectralis FAF images taken of normal and nonexudative AMD eyes were analyzed. For each image, a unique horizontally oriented FAF signature composed of vertically averaged gray-scale values was generated through the fovea. A pairwise comparison of 2 signatures was performed using a modified difference of squares method, which generated a single quantitative value, the IRA.
Main Outcomes and Measures
The effects of intersession testing, cataract extraction, pupillary dilation, focal plane, and gain settings on the IRA were assessed.
The FAF images taken of the same subjects at different times demonstrated low intersession variability of the IRA (intraclass coefficient = 0.75; 95% CI, 0.45-0.92). The IRA was affected by cataract severity, cataract extraction, small pupillary diameters (<5.5 mm), defocusing, and excessive high or low camera gain. The IRA values correlated with both subjective clinical rankings by retinal specialists (rs = 0.77). The IRA was positively correlated with AREDS score (rs = 0.73) and could statistically distinguish AREDS grades 3 and 4 (P < .001). Serial imaging demonstrated the utility of the method for identifying clinically meaningful disease progression.
Conclusions and Relevance
The IRA method applied to FAF digital files can quantify AMD disease severity and may be helpful in identifying AMD progression.