ArticleTranslational vision science & technology2025
AI Quantification of Vascular Lesions in Mouse Fundus Fluorescein Angiography.
Article in Translational vision science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Purpose: Quantifying vascular leakage in fundus fluorescein angiography (FFA) is a critical endpoint in preclinical models of diseases such as neovascular age-related macular degeneration, retinopathy of prematurity, and diabetic retinopathy. Traditional manual methods are labor intensive and prone to variability. We developed an artificial intelligence (AI)-assisted method to improve efficiency and accuracy in quantifying vascular lesions in FFA images. Methods: Nikon NIS-Elements software with AI functionality was used to create an automated FFA analysis method. FFA images were acquired using the Phoenix MICRON IV imaging system in two mouse models of ocular angiogenesis: (1) very low-density lipoprotein receptor (Vldlr) knockout mice exhibiting spontaneous pathological chorioretinal neovascularization, and (2) a laser-induced choroidal neovascularization model. The AI model was trained on manually segmented FFA images to delineate lesions and quantify lesion area and fluorescence intensity. Results: The AI model demonstrated high accuracy in quantifying vascular lesions in FFA images, achieving 99.7% agreement with manual counts. It attained a precision, recall, and F1 score of 0.94, with an intraclass correlation coefficient (ICC) of 0.991. The model showed strong spatial agreement with manual segmentations and consistent lesion area measurements. On validation images, it maintained expert-level performance (ICC = 0.998) with high sensitivity and precision. Additionally, it effectively captured temporal changes in vascular leakage by measuring lesion area and fluorescence intensity, demonstrating robustness in real-world experiments. Conclusions: Our AI model quantifies vascular lesions in FFA images with high accuracy, outperforming manual analysis. Translational Relevance: AI-based quantification provides a scalable, consistent alternative to manual methods, enhancing research efficiency.
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