DOI: 10.5061/dryad.2rbnzs7m4
title: Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning

description: 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.
description: This dataset comprises anonymised metadata and OCT segmentation
 data of patients undergoing treatment for wet AMD at Moorfields Eye
 Hospital, London, United Kingdom. The Moorfields AMD dataset for this
 study included all treatment-naive eyes that began anti-VEGF therapy for
 neovascular AMD between 1st June 2012 and 30th June 2017. Imaging data
 included macular OCT scans captured using 3DOCT-2000 devices (Topcon
 Corp., Tokyo, Japan) comprising 128 B-scans covering a volume of
 6x6x2.3mm. Patient demographics recorded in Moorfields' electronic
 medical record including age, self-reported gender identity and
 race/ethnicity, along with associated clinical metadata including visual
 acuity (VA) in ETDRS (early treatment diabetic retinopathy study) letters
 and whether an injection was administered, was available for each visit.
 Whenever an OCT scan was not available on the exact day of the first
 injection for the first-treated eye, a scan from up to 14 days prior was
 used. Second-treated eyes that sequentially converted to neovascular AMD
 and started treatment in the time period of this study were also analysed,
 with their baseline scan at their first injection visit used for analysis.
 Second-treated eyes were not required to have contributed to the
 first-treated eye cohort. All eyes were analysed independently. 
 Each row of the CSV is associated with OCT segmentation data.
 This data was obtained by inputting each OCT into a deep learning-based 3D
 segmentation network that automatically predicts segmented features
 present in each voxel of the image. Voxels can be summed and multiplied by
 the real world voxel size to provide volumetric measurements of each
 feature. For this study, the following segmented features were analysed:
 neurosensory retina (NSR), retinal pigment epithelium (RPE), IRF, SRF,
 SHRM, hyperreflective foci (HRF), drusen, fibrovascular pigment epithelium
 detachment (fvPED), and serous PED (sPED). The CST measurements were
 defined as average thickness in the central 1mm diameter circle of the
 ETDRS grid, measured in μm. The CST comprised all segmented features above
 the RPE to the inner boundary of the NSR, including SHRM, SRF, HRF, and
 IRF.
description: ReadMe file attached