Abstract
To investigate quantitative associations between AI-assessed disease activity and optical coherence tomography angiography (OCTA)-derived parameters in patients with neovascular age-related macular degeneration (nAMD) undergoing anti-VEGF therapy. OCTA and SD-OCT images obtained from multicenter, randomized study data were evaluated. A deep learning algorithm (RetInSight) was used to detect and quantify macular fluid on SD-OCT. Mixed effects models were applied to evaluate correlations between fluid volumes, macular neovascularization (MNV)-type and OCTA-derived MNV parameters; lesion size (LS) and vessel area (NVA). 230 patients were included. A significant positive correlation was observed between SRF and NVA (estimate = 199.8 nl/mm2, p = 0.023), while a non-significant but negative correlation was found between SRF and LS (estimate = − 71.3 nl/mm2, p = 0.126). The presence of Type I and Type II MNV was associated with significantly less intraretinal fluid (IRF) compared to Type III MNV (estimate type I:− 52.1 nl, p = 0.019; estimate type II:− 51.7 nl, p = 0.021). A significant correlation was observed between pigment epithelial detachment (PED) and the interaction between NVA and LS (estimate:28.97 nl/mm2; p = 0.012). Residual IRF at week 12 significantly correlated to baseline NVA (estimate:38.1 nl/mm2; p = 0.015) and LS (estimate:− 22.6 nl/mm2; p = 0.012). Fluid in different compartments demonstrated disparate associations with MNV OCTA features. While IRF at baseline was most pronounced in type III MNV, residual IRF was driven by neovascular MNV characteristics. Greater NVA in proportion to LS was associated with higher amounts of SRF and PED. The correlation between these parameters may represent MNV maturation and can be used as a biomarker for resolution of disease activity. AI-based OCT analysis allows for a deeper understanding of neovascular disease in AMD and the potential to adjust therapeutic strategies to optimize outcomes through precision medicine.
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