Automated Retinal Image Analysis for Evaluation of Focal Hyperpigmentary Changes in Intermediate Age Related Macular Degeneration
Purpose To develop and evaluate a software tool for automated detection of focal hyperpigmentary changes (FHC) in eyes with intermediate age related macular degeneration (AMD) Methods Color fundus (CFP) and autofluorescence (AF) photographs of 33 eyes with FHC of 28 AMD patients (mean age 71 years) from the prospective longitudinal natural history MODIAMD study were included. Fully automated to semiautomated registration of baseline to corresponding follow up images was evaluated. Following the manual circumscription of individual FHC (four different readings by two readers) a machine learning algorithm was evaluated for automatic FHC detection. Results The overall pixel distance error for the semiautomated (CFP follow up to CFP baseline: median 5.7; CFP to AF images from the same visit: median 6.5) was larger as compared for the automated image registration (4.5 and 5.7; P 0.001 and P 0.001) The total number of manually circumscribed objects and the corresponding total size varied between 637 to 1163 and 520,848 pixels to 924,860 pixels, respectively. Performance of the learning algorithms showed a sensitivity of 96% at a specificity level of 98% using information from both CFP and AF images and defining small areas of FHC "speckle appearance" as "neutral." Conclusions FHC as a high risk feature for progression of AMD to late stages can be automatically assessed at different time points with similar sensitivity and specificity as compared to manual outlining. Upon further development of the research prototype, this approach may be useful both in natural history and interventional large scale studies for a more refined classification and risk assessment of eyes with intermediate AMD. Translational Relevance Automated FHC detection opens the door for a more refined and detailed classification and risk assessment of eyes with intermediate AMD in both natural history and future interventional studies.
Translational vision science technology