A Deep Learning Approach for Automated Detection of Geographic Atrophy from Color Fundus Photographs.

ABSTRACT:

Purpose

To assess the utility of deep learning in the detection of geographic atrophy (GA) from color fundus photographs and to explore potential utility in detecting central GA (CGA).

Design

A deep learning model was developed to detect the presence of GA in color fundus photographs, and 2 additional models were developed to detect CGA in different scenarios.

Participants

A total of 59 812 color fundus photographs from longitudinal follow-up of 4582 participants in the Age-Related Eye Disease Study (AREDS) dataset. Gold standard labels were from human expert reading center graders using a standardized protocol.

Methods

A deep learning model was trained to use color fundus photographs to predict GA presence from a population of eyes with no AMD to advanced AMD.

SUBMITTER: Keenan TD 

PROVIDER: S-EPMC6810830 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature