A deep learning model for segmentation of geographic atrophy to study its long-term natural history

15 Aug 2019Bart LiefersJohanna M. ColijnCristina González-GonzaloTimo VerzijdenPaul MitchellCarel B. HoyngBram van GinnekenCaroline C. W. KlaverClara I. Sánchez

Purpose: To develop and validate a deep learning model for automatic segmentation of geographic atrophy (GA) in color fundus images (CFIs) and its application to study growth rate of GA. Participants: 409 CFIs of 238 eyes with GA from the Rotterdam Study (RS) and the Blue Mountain Eye Study (BMES) for model development, and 5,379 CFIs of 625 eyes from the Age-Related Eye Disease Study (AREDS) for analysis of GA growth rate... (read more)

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