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

Abstract

Purpose: 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.Design: Retrospective analysis of OCT images from the Moorfields Eye Hospital AMD Database.Participants: A total of 2473 first-treated eyes and 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 firstand second-treated eyes by visual acuity (VA) and race/ethnicity and correlations between volumes.Main Outcome Measures: Volumes of segmented features (mm 3 ) and central subfield thickness (CST) (mm).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 majority of features were associated with worse VA.Conclusions: We report the results of large-scale automated quantification of a novel range of baseline features in neovascular AMD. Major differences between firstand 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 structureefunction correlations. These data will be made publicly available for replication and future investigation by the AMD research community.

Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning

“…Segmentations were acceptable to specialists in most cases, and qualitative evaluation provided valuable insights in addition to more traditional quantitative analysis. This tool has already advanced the understanding of the anatomical characteristics in AMD 38,41 and in the future may provide novel quantitative end points for clinical trials to enable in-depth analy-sis. Automated segmentation systems offer the potential to transform clinical workflows; however, further research is needed to directly assess their utility for disease monitoring and management.…”

Inflammatory Factors of Macular Atrophy in Eyes With Neovascular Age-Related Macular Degeneration Treated With Aflibercept

“…In post-hoc analyses of clinical trials (57)(58)(59)(60) and real-world study (61), certain baseline morphological parameters including IRF, SRF, subretinal hyperreflective material (SHRM), and pigment epithelium detachment (PED) were associated with visual outcomes in nAMD eyes beginning anti-VEGF therapy. Therefore, IRF, SRF, SHRM, and PED have been recognized as significant indicators of disease activity in macular neovascularization (62), and the elevations of IRF and age are expected as negative prognostic impact on visual outcomes (57,58,63,64). As for the development of MA in nAMD eyes under anti-VEGF therapy, IRF in foveal center at baseline was identified as a risk factor for MA incidence in the Comparison of Age-Related Macular Degeneration Treatments Trials (12).…”

Artificial intelligence-based predictions in neovascular age-related macular degeneration

“…Moorfields Eye Hospital, a tertiary referral retinal center in the United Kingdom, maintains a real-world database of electronic medical records and associated OCT images from patients with AMD treated with at least one ranibizumab or aflibercept injection from 2008 to 2018 and with at least 1 year of follow-up [ 23 ]. Altogether, the Moorfields AMD dataset includes 8174 eyes of 6664 patients; a de-identified version of the segmentation results is openly available to the research community [ 23 , 24 ▪ ].…”

“…A key model for OCT image segmentation and disease classification, developed by De Fauw and colleagues [ 25 ], uses a deep learning-based framework with two independent networks to perform automated diagnosis of retinal diseases on OCT scans. This methodology has been applied to investigating imaging biomarkers and visual outcomes [ 24 ▪ , 26 ▪▪ ]. Following this, another group developed a novel automated segmentation model using a convolutional neural network [ 27 ▪ ].…”