1. A deep learning model for automated segmentation of geographic atrophy imaged with swept-source optical coherence tomography images

    A deep learning model for automated segmentation of geographic atrophy imaged with swept-source optical coherence tomography images

    Purpose: To present a deep-learning algorithm for the segmentation of geographic atrophy (GA) using en face swept-source optical coherence tomography (SS-OCT) images that is accurate and reproducible for the assessment of GA growth over time. Design: Retrospective review of images obtained as part of a prospective natural history study Subjects: Patients with GA (90), early/intermediate AMD (32), healthy controls (16) METHODS: An automated algorithm utilizing scan volume data to generate three image inputs characterizing the main OCT features of GA - hyper-transmission in sub RPE slab, regions of RPE loss, and loss of retinal thickness - was trained with 126 images ...

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