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 comparisons
between first and second eyes, by visual acuity (VA) and by
race/ethnicity, and correlations between volumes. Main outcome measures:
Volumes of segmented features (mm3), central subfield
thickness (CST). Results: In first-treated eyes, the majority had
both IRF and SRF (54.7%). First-treated eyes had greater volumes
for all segmented tissues, with the exception of drusen, which was
greater in second-treated eyes. In first-treated eyes, older age
was associated with lower volumes for RPE, SRF, NSR and sPED; in
second-treated eyes, older age was associated with lower volumes of NSR,
RPE, sPED, fvPED and SRF. Eyes from black individuals had higher SRF, RPE
and serous PED volumes, compared with other ethnic groups. Greater volumes
of the vast majority of features were associated with worse VA.
Conclusion: We report the results of large scale automated quantification
of a novel range of baseline features in neovascular AMD. Major
differences between first and second-treated eyes, with increasing age,
and between ethnicities are highlighted. In the coming years, enhanced,
automated OCT segmentation may assist personalization
of real-world care, and the detection of novel structure-function
correlations. These data will be made publicly available for replication
and future investigation by the AMD research community.