Automated Quantification of Decreased FAF in Stargardt Disease: Validation of a Novel Method Compared to Manual Grading Standards

PurposeTo evaluate the repeatability and reproducibility of a novel automated method compared with manual segmentation for measuring decreased autofluorescence (DAF) and definitely decreased autofluorescence (DDAF) in fundus autofluorescence (FAF) images of patients with Stargardt disease. DesignCross-sectional reproducibility and agreement study. ParticipantsA total of 316 eyes from 158 genetically confirmed Stargardt patients were analyzed. For intra-grader repeatability, 114 FAF images were reassessed in a masked, repeated-measures design. MethodsDAF and DDAF lesion areas were independently quantified by five certified graders using either manual delineation with Heidelberg RegionFinder or a threshold-based automated algorithm. Agreement and repeatability were assessed using intraclass correlation coefficients (ICC), standard error of measurement (SEM), minimal detectable change (MDC), Lins concordance correlation coefficient (CCC), Bland-Altman plots, and Passing-Bablok regression. Both raw and square-root-transformed lesion areas were evaluated. Main Outcome MeasuresRepeatability (intra-grader ICC, SEM, MDC), reproducibility (inter-grader ICC), and agreement (CCC, bias in regression analysis) between and within manual and automated methods. ResultsThe automated method achieved excellent intra-grader repeatability for both DAF and DDAF (ICCs [≥]0.988, SEM [≤]0.71 mm{superscript 2}, MDC [≤]1.98 mm{superscript 2}), with minimal operator influence. Manual measurements showed variable repeatability (DAF ICCs 0.909-0.974; DDAF ICCs as low as 0.837), with square-root transformation reducing SEM and MDC. Inter-grader reproducibility was highest for automated methods (ICC = 0.989-0.992), whereas manual methods ranged from 0.764-0.939 (raw) and 0.867-0.922 (transformed). Cross-method agreement was strong (CCC = 0.91-0.96), though minor proportional and constant bias was observed in raw DAF data. ConclusionsThe automated approach provides near-perfect repeatability and high agreement with manual grading, offering a scalable, objective alternative for quantifying hypo-autofluorescent lesions in Stargardt disease. Manual methods are generally reliable but more variable, especially for DDAF, and benefit from square-root transformation.