Automated fluid monitoring to optimize the follow-up of neovascular age-related macular degeneration patients in the Brazilian population.

Abstract

Objectives: To investigate the efficacy of an artificial intelligence (AI)-based fluid monitoring tool in optimizing the monitoring of neovascular age-related macular degeneration (nAMD) patients in a Brazilian cohort.

Methods: This is a retrospective real-world study performed in a tertiary center in Brazil, including patients with nAMD. Spectral-domain optical coherence tomography (Spectralis, Heidelberg Engineering, Germany) images were processed at baseline and over 2 years of follow-up. Demographic and clinical data were collected. A deep learning algorithm (Fluid Monitor, RetInSight, Austria) was used to automatically quantify intraretinal fluid (IRF), subretinal fluid (SRF) and pigment epithelial detachment (PED). A longitudinal panel regression model and Log-Rank test were performed to assess the correlation between fluid volumes and treatment frequency, visual outcomes, macular atrophy (MA) and subretinal fibrosis (SF) development.

Results: Ninety-nine eyes from 84 patients were included. Fifty-eight eyes were treatment-naïve. Higher IRF and PED in the 6 mm area were correlated with worse visual outcomes over a 2-year follow-up (p = 0.01 and p < 0.001, respectively). Higher IRF, SRF and PED were correlated with an increased risk of SF development (p < 0.001, p = 0.049 and p = 0.02 respectively). MA development showed no significant correlation with higher IRF, SRF nor PED in this analysis. Higher SRF volume correlated with a greater number of required intravitreal injections over 2-years.

Conclusion: This study investigates the multifaceted landscape of nAMD in a tertiary center in the Southeast Brazil using an AI-based fluid monitoring tool. Further studies that highlight the significance of using newly validated technologies across diverse populations worldwide will be of interest.