Abstract

To compare area measurements between swept source optical coherence tomography angiography (SSOCTA), fluorescein angiography (FA), and indocyanine green angiography (ICGA) after applying a novel deep-learning-assisted algorithm for accurate image registration. We applied an algorithm for the segmentation of blood vessels in FA, ICGA, and SSOCTA images of 24 eyes with treatment-naive neovascular age-related macular degeneration. We trained a model based on U-Net and Mask R-CNN for each imaging modality using vessel annotations and junctions to estimate scaling, translation, and rotation. For fine-tuning of the registration, vessels and the elastix framework were used. Area, perimeter, and circularity measurements were performed manually using ImageJ. Choroidal neovascularization lesion size, perimeter, and circularity delineations showed no significant difference between SSOCTA and ICGA (all P > 0.05). Choroidal neovascularization area showed excellent correlation between SSOCTA and ICGA (r = 0.992) and a Bland-Altman bias of -0.10 ± 0.24 mm. There was no significant difference in foveal avascular zone size between SSOCTA and FA (P = 0.96) and an extremely small bias of 0.0004 ± 0.04 mm and excellent correlation (r = 0.933). Foveal avascular zone perimeter was not significantly different, but foveal avascular zone circularity was significantly different (P = 0.047), indicating that some small cavities or gaps may be missed leading to higher circularity values representing a more round-shaped foveal avascular zone in FA. We found no statistically significant differences between SSOCTA and FA and ICGA area measurements in patients with treatment-naive neovascular age-related macular degeneration after applying a deep-learning-assisted approach for image registration. These findings encourage a paradigm shift to using SSOCTA as a first-line diagnostic tool in neovascular age-related macular degeneration.

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