DOI: 10.5061/dryad.8cz8w9h0v
title: Data from: Quantifying changes on optical coherence tomography in eyes receiving treatment for neovascular age-related macular degeneration

description: Purpose Application of artificial intelligence (AI) to macular optical
 coherence tomography (OCT) scans to segment and quantify volumetric change
 of anatomical and pathological features during intravitreal treatment for
 neovascular age-related macular degeneration (AMD). Design Retrospective
 analysis of OCT images from the Eye Hospital AMD Database. Participants
 2115 eyes from 1883 patients that started anti-vascular endothelial growth
 factor (anti-VEGF) treatment between 1st June 2012 and 30th June 2017.
 Methods The Eye Hospital neovascular AMD database was queried for first
 and second eyes that received anti-VEGF treatment and had an OCT scan at
 baseline and at 12 months. Follow-up scans were input into the AI system
 to derive volumetric outputs for the following variables: intraretinal
 fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED),
 subretinal hyperreflective material (SHRM), hyperreflective foci (HRF),
 neurosensory retina (NSR) and retinal pigment epithelium (RPE). Volumes
 were studied at different time points and comparisons made with respect to
 baseline volume groups. Cross-sectional comparisons between time points
 were conducted using Mann-Whitney U test. Results Mean volumes of analysed
 features decreased significantly from baseline to both four and 12 months,
 in both first and second-treated eyes. Pathological features that reflect
 exudation, including pure fluid components (IRF and SRF) and those with a
 mixture of fluid and fibrovascular tissue (PED and SHRM), displayed
 similar response to treatment over 12 months. Mean PED and SHRM volumes
 showed less pronounced but also substantial decreases over the first two
 months, reaching a plateau post loading phase, and minimal change to 12
 months. Both NSR and RPE volumes showed gradual reductions over time, and
 not as substantial as exudative features. Conclusion We report the results
 of a quantitative analysis of change in retinal segmented features over
 time, enabled by an AI segmentation system. Cross-sectional analysis at
 multiple time points demonstrated significant associations between
 baseline OCT-derived segmented features and the volume of biomarkers at
 follow-up. Demonstrating how certain OCT biomarkers progress with
 treatment and the impact of pre-treatment retinal morphology on different
 structural volumes may provide novel insights into disease mechanisms and
 aid personalization of care. Data will be made public for future studies.
description: The dataset comprises anonymised metadata and OCT
 segmentation data of patients undergoing treatment for wet
 AMD.
description: # Data from: Quantifying changes on optical coherence tomography in eyes
 receiving treatment for neovascular age-related macular degeneration ##
 INTRODUCTION This CSV dataset (AMD_longitudinal_MEH_v1.csv) is associated
 with the paper Moraes et al. Quantifying changes on optical coherence
 tomography in eyes receiving treatment for neovascular age-related macular
 degeneration. Ophthalmology Science. (2024). Available at:
 [https://doi.org/10.1016/j.xops.2024.100570](https://doi.org/10.1016/j.xops.2024.100570) The dataset comprises anonymised metadata and OCT segmentation data of patients undergoing treatment for wet AMD. ## DATA FIELDS ID Anonymous ID - an integer indicating the ID of the patient  DaysSinceBaseline Replaces visit dates of patient. Baseline is denoted as '0' for the day the first eye was treated with an injection Eye Left or Right FirstTreatedEye The eye that first received an injection (Left or Right) Gender Male or female Neurosensory Number of voxels segmented as the feature neurosensory retina (NSR) RPE Number of voxels segmented as the feature retinal pigment epithelium (RPE) IRF Number of voxels segmented as the feature intraretinal fluid (IRF) SRF Number of voxels segmented as the feature subretinal fluid (SRF) SHRM Number of voxels segmented as the feature subretinal hyperreflective material (SHRM) Drusenoid_PED Number of voxels segmented as the feature drusen Serous_PED Number of voxels segmented as the feature serous pigment epithelium detachment (sPED) Fibrovascular_PED Number of voxels segmented as the feature fibrovascular pigment epithelium detachment (fvPED) grouped_ethnicity Ethnic groups combined into: 'White', 'Black', 'Asian', 'Other or unknown' grouped_VA Visual acuity groups  grouped_age Age groups of 50-59, 60-69, 70-79, &gt;80, or NULL if unknown. NOTES \* Cells with NA indicate missing data \* Each voxel equates to 2.60 x 11.72 x 47.24 μm in the A-scan, B-scan, and C-scan directions, respectively \* Each eye included in the dataset is given a unique ID. To ensure anonymisation, it is not possible to link a patient’s two eyes if the individual has both eyes in the dataset \* All OCT data is captured using 3DOCT-2000 devices (Topcon Corp., Tokyo, Japan). All images comprise 512*885*128 voxels covering a volume of 6x6x2.3mm \* Segmentation data was output using a deep learning segmentation model described further in De Fauw et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine (2018); and Yim and Chopra et al. Predicting conversion to wet age-related macular degeneration using deep learning. Nature Medicine. (2020) ## Version changes 24-Oct-2024  Provided IDs for longitudinal tracking.