1. Doheny Eye Institute Receives NIH Grant for Artificial Intelligence for Assessment of Stargardt Macular Atrophy

    Doheny Eye Institute Receives NIH Grant for Artificial Intelligence for Assessment of Stargardt Macular Atrophy

    Doheny Eye Institute Receives a 2020 NIH Grant for $235,500 for Artificial Intelligence for Assessment of Stargardt Macular Atrophy. The principal investigator is  Zhihong Hu. Below is a summary of the proposed work. 

    Stargardt disease is the most frequent form of inherited juvenile macular degeneration. Fundus autofluorescence (FAF) is a widely available imaging technique which may aid in the diagnosis of Stargardt disease and is commonly used to monitor its progression. FAF imaging provides an in vivo assay of the retinal layers, but is only an indirect measure. Spectral-domain optical coherence tomography (SD-OCT), in contrast, provides three-dimensional visualization of the retinal microstructure, thereby allowing it to be assessed directly and individually in eyes with Stargardt disease. At a retinal disease endpoints meeting with the Food and Drug Administration (FDA) in November of 2016, a reliable measure of the anatomic status of the integrity of the ellipsoid zone (EZ) in the retina, was proposed to be a potential suitable regulatory endpoint for therapeutic intervention clinical trials. Manual segmentation/identification of the EZ band, particularly in 3-D OCT images, has proven to be extremely tedious, time-consuming, and expensive. Automated objective segmentation techniques, such as an approach using a deep learning - artificial intelligence (AI) construct, would be of significant value. Moreover, Stargardt disease may cause severe visual loss in children and young adults. Early prediction of Stargardt disease progression may facilitate new therapeutic trials. Thus, this proposal develops an AI-based approach for automated Stargardt atrophy segmentation and the prediction of atrophy progression in FAF and OCT images. More specifically, we first register the longitudinal FAF and OCT enface images respectively, and register the cross-sectional FAF to OCT image. We then develop a 2-D approach for Stargardt atrophy segmentation from FAF images using an AI approach and a 3-D approach for EZ band segmentation from OCT images using a 3-D graph-based approach. Finally, an AI-based approach is developed to predict subsequent development of new Stargardt atrophy or progression of existing atrophy from the OCT EZ band thickness and intensity features of the current patient visit.

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