Influence Of Artifical Intelligence-Based Retinal Fluid Monitoring On The Photoreceptor Condition

Patients who undergo cataract surgery frequently also show signs of age-related macular degeneration (AMD). The disease activity of AMD should be evaluated at the pre-surgical examination to avoid suboptimal outcomes after surgery. Especially the photoreceptor condition has a significant influence on the visual outcomes after cataract surgery. The purpose of this study was to quantify photoreceptor integrity loss during anti-VEGF therapy for neovascular AMD (nAMD) and correlate these findings with disease activity using precise artificial intelligence fluid quantifications.

This study is a post-hoc analysis of data from the Fight Retinal Blindness! (FRB!) registry in Zurich.

Spectral domain optical coherence tomography (SD-OCT) (Spectralis, Heidelberg Engineering, Germany) images of treatment-naïve patients with nAMD were processed at baseline and during follow-up of 3 years. A deep learning-based algorithm (Vienna Fluid Monitor, RetInSight, Austria) was used to automatically quantify intraretinal fluid (IRF), subretinal fluid (SRF) and pigment epithelial detachment (PED) volumes. Spatiotemporal correlation of fluid volumes with photoreceptor integrity loss was performed to identify early signs of atrophy progression in nAMD.

Two hundred and eleven eyes from 158 patients were included which developed atrophy. The mean ± SD photoreceptor integrity loss area in the central 6 mm was 1.81 ± 2.68 µm² at baseline, increased to 4.21 ± 4.45 µm² in the first year and reached 6.21 ± 6.15 µm² at month 36. The mean photoreceptor thickness in the central 6 mm at baseline was 26.9 ± 4.7 µm and decreased to 21.4 ± 5.8 µm at month 36. Higher fluid volume (top 25%) of IRF and PED in the central 1mm and 6mm of the macula were significantly associated with more advanced photoreceptor thinning compared to the low fluid volume group (low 75%) during follow-up. However, SRF volumes showed no impact on photoreceptor thickness or integrity loss.

The identification of early signs of atrophy in clinical practice is an important step towards a precise and personalized care to better estimate a patient’s prognosis after surgery. Detection of photoreceptor thinning and early loss of integrity dependent on retinal fluid behaviour adds substantially to our understanding of disease progression in AMD patients. Artificial intelligence tools are best suited to model individual disease progression utilizing clinical and subclinical biomarker quantification in the real world in a fast, objective and precise manner.