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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.
588 views reported since publication in 2020.