Correlation of retinal fluid and photoreceptor and RPE loss in neovascular AMD by automated quantification, a real-world FRB! analysis

Abstract

Purpose: To quantify ellipsoid zone (EZ) loss during anti-VEGF therapy for neovascular age-related macular degeneration (nAMD) and correlate these findings with nAMD disease activity using artificial intelligence-based algorithms.

Methods: Spectral domain optical coherence tomography (Spectralis, Heidelberg Engineering) images from nAMD treatment-naïve patients from the Fight Retinal Blindness! (FRB!) Registry from Zürich, Switzerland were processed at baseline and over 3 years of follow-up. An approved deep learning algorithm (Fluid Monitor, RetInSight) was used to automatically quantify intraretinal fluid (IRF), subretinal fluid (SRF) and pigment epithelial detachment (PED). An ensemble U-net deep learning algorithm was used to automated quantify EZ integrity based on EZ layer thickness. The impact of fluid volumes on EZ thickness and late-stages outcomes were calculated using Wilcoxon rank-sum tests, a linear mixed model and a longitudinal panel regression model.

Results: Two hundred and eleven eyes from 158 patients were included. The mean ± SD EZ loss area in the central 6 mm was 1.81 ± 2.68 mm² at baseline and reached 6.21 ± 6.15 mm² at month 36. Higher fluid volumes (top 25%) of IRF and PED in the central 1 and 6 mm of the macula were significantly associated with more advanced EZ thinning and loss compared to the low fluid volume subgroup. The high SRF subgroup in the linear regression model showed no statistically significant association with EZ integrity in the central macula; however, the longitudinal analysis revealed an increased EZ thickness with no additional loss.

Conclusions: Intraretinal fluid and PED volumes and their resolution pattern have an impact on alteration of the underlying EZ layer. AI-supported quantifications are helpful in quantifying early signs of macular atrophy and providing individual risk profiles as a basis for tailored therapies for optimized visual outcomes.

Keywords: anti‐VEGF; artificial intelligence; geographic atrophy; image analysis; machine learning; neovascular AMD; optical coherence tomography; photoreceptor; retina.

FIGURE 1

Automated segmentation of intraretinal fluid (red), subretinal fluid (yellow) and pigment epithelial detachment (blue), and ellipsoid zone (EZ) thickness. EZ thickness is reliably segmented when the fluid volume is below the threshold of 60 nL in the central 6 mm.

FIGURE 2

The plots show a continuous thinning on the ellipsoid zone (EZ) (a), a progressive increase in the EZ loss area (b) and a decrease in visual acuity particularly after month 15 (c).

FIGURE 3

A comparison between higher fluid volume subgroup (top 25%) in each compartment (intraretinal and subretinal fluid and pigment epithelial detachment) and bottom 75% fluid volume subgroup regarding ellipsoid zone (EZ) thickness and EZ loss during follow‐up.