Computational quantification of complex fundus phenotypes in age-related macular degeneration and Stargardt disease.

Purpose: To describe an automated method of quantification of specific fundus phenotypes and evaluate its performance in differentiating drusen, the hallmark lesions of age-related macular degeneration (AMD), from similar-looking bright lesions, the pisciform deposits or flecks typical of Stargardt disease (SD).
Methods: Fundus macular images of 30 eyes of 30 subjects were studied. Fifteen subjects had a clinical diagnosis of AMD with at least 10 intermediate and/or 1 large drusen, and the other 15 had SD. As a test of bright-lesion separation, AMD and SD subjects were chosen from the heterogeneous phenotypes of each disorder, to be as visually similar as possible. Drusen and fleck properties were quantified from the color images by using an automated method, and a shape classifier was used to divide the images as characteristic of either AMD or SD. Image identification performance was quantified by using the area under the receiver operating characteristic curve (AUC).
Results: All SD subjects demonstrated at least one disease-associated variant of the ABCA4 gene. The method achieved an AUC of 0.936 for differentiating AMD from SD.
Conclusions: Automated quantification of fundus phenotypes was achieved, and the results show that the method can differentiate AMD from SD, two distinctly different genetically associated disorders, by quantifying the properties of the bright lesions (drusen and flecks) in their fundus images, even when the images were visually selected to be similar. Quantification of fundus phenotypes may allow recognition of new phenotypes, correlation with new genotypes and may measure disease-specific biomarkers to improve management of patients with AMD or SD.