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Kathryn Pepple, Qing Nie, Sally Ong, Scott Cousins; Detection and Quantification of Autofluorescence Abnormalities in Patients with Neovascular Macular Degeneration using a Fully Automated Image Analysis Algorithm. Invest. Ophthalmol. Vis. Sci. 2013;54(15):4089.
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To develop a fully automated fundus autofluorescence (AF) image analysis algorithm and evaluate the occurrence of retinal pigment epithelium abnormalities (RPE) in patients with neovascular macular degeneration.
A retrospective chart review was performed to identify patients with NVAMD that had fundus AF images obtained at presentation and at follow up at least one year later. A custom Matlab algorithm was designed to allow fully automated segmentation and quantification of hypo-AF and hyper-AF. The computer segmentation results were compared against the boundaries defined by three expert graders who performed manual segmentation. The baseline area of abnormal AF was compared to the follow up area and a rate of change was calculated.
22 eyes of 16 patients with NVAMD were identified. The computer algorithm successfully identified areas of hyper-AF and hypo-AF in all eyes. The area identified by the computer algorithm had good agreement with the area identified by the human graders. In all eyes at baseline, abnormal hypo-AF was present, and on average, the area increased in size by the final follow up. The average size of the hyper- AF area also increased from baseline, but less than the area of hypo-AF. An example of the output from the automated software is included below.
Image analysis of fundus AF using a fully automated algorithm can provide rapid, objective information about RPE pathology in patients with NVAMD. This software has the potential to allow for monitoring of RPE changes to detect disease progression and in response to medical therapy.
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