Related Experiment Video
Updated: Sep 16, 2025
Application of Optical Coherence Tomography to a Mouse Model of Retinopathy
Published on: January 12, 2022
Quantification of Optical Coherence Tomography Features in >3500 Patients with Inherited Retinal Disease Reveals
William Woof1,2, Thales A C de Guimarães1,2,3, Saoud Al-Khuzaei4
1Institute of Ophthalmology, University College London, 11-43 Bath Street, London, UK.
Purpose:
To quantify spectral-domain optical coherence tomography (SD-OCT) images cross-sectionally and longitudinally in a large cohort of molecularly characterized patients with inherited retinal disease (IRDs) from the UK.
Design:
Retrospective study of imaging data.
Participants:
Patients with a clinical and molecularly confirmed diagnosis of IRD who have undergone macular SD-OCT imaging at Moorfields Eye Hospital (MEH) between 2011 and 2019. We retrospectively identified 4,240 IRD patients from the MEH database (198 distinct IRD genes), including 69,664 SD-OCT macular volumes.
Methods:
Eight features of interest were defined: retina, fovea, intraretinal cystic spaces (ICS), subretinal fluid (SRF), subretinal hyper-reflective material (SHRM), pigment epithelium detachment (PED), ellipsoid zone loss (EZ-loss) and retinal pigment epithelium loss (RPE-loss). Manual annotations of five b-scans per SD-OCT volume was performed for the retinal features by four graders based on a defined grading protocol. A total of 1,749 b-scans from 360 SD-OCT volumes across 275 patients were annotated for the eight retinal features for training and testing of a neural-network-based segmentation model, AIRDetect-OCT, which was then applied to the entire imaging dataset.
Main Outcome Measures:
Performance of AIRDetect-OCT, comparing to inter-grader agreement was evaluated using Dice score on a held-out dataset. Feature prevalence, volume and area were analysed cross-sectionally and longitudinally.
Results:
The inter-grader Dice score for manual segmentation was ≥90% for retina, ICS, SRF, SHRM and PED, >77% for both EZ-loss and RPE-loss. Model-grader agreement was >80% for segmentation of retina, ICS, SRF, SHRM, and PED, and >68% for both EZ-loss and RPE-loss. Automatic segmentation was applied to 272,168 b-scans across 7,405 SD-OCT volumes from 3,534 patients encompassing 176 unique genes. Accounting for age, male patients exhibited significantly more EZ-loss (19.6mm2 vs 17.9mm2, p<2.8×10-4) and RPE-loss (7.79mm2 vs 6.15mm2, p<3.2×10-6) than females. RPE-loss was significantly higher in Asian patients than other ethnicities (9.37mm2 vs 7.29mm2, p<0.03). ICS average total volume was largest in RS1 (0.47mm3) and NR2E3 (0.25mm3), SRF in BEST1 (0.21mm3) and PED in EFEMP1 (0.34mm3). BEST1 and PROM1 showed significantly different patterns of EZ-loss (p<10-4) and RPE-loss (p<0.02) comparing the dominant to the recessive forms. Sectoral analysis revealed significantly increased EZ-loss in the inferior quadrant compared to superior quadrant for RHO (Δ=-0.414 mm2, p=0.036) and EYS (Δ=-0.908 mm2, p=1.5×10-4). In ABCA4 retinopathy, more severe genotypes (group A) were associated with faster progression of EZ-loss (2.80±0.62 mm2/yr), whilst the p.(Gly1961Glu) variant (group D) was associated with slower progression (0.56 ±0.18 mm2/yr). There were also sex differences within groups with males in group A experiencing significantly faster rates of progression of RPE-loss (2.48 ±1.40 mm2/yr vs 0.87 ±0.62 mm2/yr, p=0.047), but lower rates in groups B, C, and D.
Conclusions:
AIRDetect-OCT, a novel deep learning algorithm, enables large-scale OCT feature quantification in IRD patients uncovering cross-sectional and longitudinal phenotype correlations with demographic and genotypic parameters.
