Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning

Purpose: To apply a deep learning algorithm for automated, objective, and comprehensive quantification of optical coherence tomography (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: 2473 first-treated eyes and another 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 hyperreflective material (SHRM), retinal pigment epithelium (RPE), hyperreflective foci (HRF), fibrovascular pigment epithelium detachment(fvPED), and serous PED (sPED). Analyses included compariso...