Purpose: The purpose of this study was to summarize and evaluate artificial intelligence (AI)
algorithms used in geographic atrophy (GA) diagnostic processes (eg isolating lesions or …

Purpose To determine whether a quantitative approach to assessment of the severity of
diabetic retinopathy (DR) lesions on ultrawide field (UWF) images can provide new …

Purpose: To automatically and accurately segment geographic atrophy (GA) in spectral-
domain optical coherence tomography (SD-OCT) images by constructing a voting system …

Purpose To develop and validate a deep learning model for the automatic segmentation of
geographic atrophy (GA) using color fundus images (CFIs) and its application to study the …

Purpose To automatically classify retinal atrophy according to its etiology, using fundus
autofluorescence (FAF) images, using a deep learning model. Methods In this study, FAF …

Purpose: To examine deep learning (DL)–based methods for accurate segmentation of
geographic atrophy (GA) lesions using fundus autofluorescence (FAF) and near-infrared …

Introduction Age-related macular degeneration (AMD) is a leading cause of irreversible
visual impairment worldwide. The endpoint of AMD, both in its dry or wet form, is macular …

Background: Age-related macular degeneration (AMD), left untreated, is the leading cause
of vision loss in people older than 55. Severe central vision loss occurs in the advanced …

Age-related macular degeneration (AMD) and Stargardt disease are the leading causes of
blindness for the elderly and young adults respectively. Geographic atrophy (GA) of AMD …

Purpose: This study describes the development of a deep learning algorithm based on the U-
Net architecture for automated segmentation of geographic atrophy (GA) lesions in fundus …