Quantitative Analysis of OCT for Neovascular Age-Related Macular Degeneration Using Deep Learning.

ABSTRACT:

Purpose

To apply a deep learning algorithm for automated, objective, and comprehensive quantification of OCT scans to a large real-world dataset of eyes with neovascular age-related macular degeneration (AMD) and make the raw segmentation output data openly available for further research.

Design

Retrospective analysis of OCT images from the Moorfields Eye Hospital AMD Database.

Participants

A total of 2473 first-treated eyes and 493 second-treated eyes that commenced therapy for neovascular AMD between June 2012 and June 2017.

Methods

A deep learning algorithm was used to segment all baseline OCT scans. Volumes were calculated for segmented features such as neurosensory retina (NSR), drusen, intraretinal fluid (IRF), subretinal fluid (SRF), subretinal hyperrefl

SUBMITTER: Moraes G 

PROVIDER: S-EPMC8528155 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature