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

Gabriella Moraes,Dun Jack Fu,Marc Wilson,Hagar Khalid,Siegfried K Wagner,Edward Korot,Daniel Ferraz,Livia Faes,Christopher J Kelly,Terry Spitz,Praveen J Patel,Konstantinos Balaskas,Tiarnan D.L Keenan,Pearse A Keane,Reena Chopra

doi:10.1016/j.ophtha.2020.09.025

Abstract

PurposeTo 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.DesignRetrospective analysis of OCT images from the Moorfields Eye Hospital AMD Database.ParticipantsA total of 2473 first-treated eyes and 493 second-treated eyes that commenced therapy for neovascular AMD between June 2012 and June 2017.MethodsA 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-treated eyes by visual acuity (VA) and race/ethnicity and correlations between volumes.Main Outcome MeasuresVolumes of segmented features (mm3) and central subfield thickness (CST) (μm).ResultsIn 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 majority of features were associated with worse VA.ConclusionsWe 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.

Highlights

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
In first-treated eyes, older age was associated with lower volumes for retinal pigment epithelium (RPE), subretinal fluid (SRF), neurosensory retina (NSR), and serous pigment epithelium detachment (PED) (sPED); in second-treated eyes, older age was associated with lower volumes of NSR, RPE, sPED, fibrovascular pigment epithelium detachment (fvPED), and SRF
Eyes from Black individuals had higher SRF, RPE, and serous PED volumes compared with other ethnic groups

Summary

Methods

The Moorfields AMD dataset for this study included all treatmentnaive eyes that began anti-VEGF therapy for neovascular AMD between June 1, 2012, and June 30, 2017.27,28 Imaging data included macular OCT scans captured using 3DOCT-2000 devices (Topcon Corp, Tokyo, Japan), comprising 128 B-scans covering a volume of 6 Â 6 Â 2.3 mm. Second-treated eyes that sequentially converted to neovascular AMD and started treatment in the time period of this study were analyzed, with their baseline scan at their first injection visit used for analysis. If multiple scans were present on the same visit, the scan with the lowest volume of mirror and blink artefacts was selected for analysis When neither of these artefacts existed, the scan with the lowest volume of padding artefact, indicating less manipulation performed by the OCT device software during postprocessing and a cleaner image capture, was selected.

Results

Discussion

Conclusion