Deep Learning-Guided Retinal Vascular Morphometric Quantification in Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy Mouse Models.

Purpose: To develop and validate an explainable deep learning-guided workflow to localize and quantify focal retinal luminal pathology on fundus fluorescein angiography (FFA) in NOTCH3 variant knock-in mouse models of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. Design: Cross-sectional experimental imaging and computational analysis. Subjects: Thirty-two mice (wild-type [WT] n = 12; NOTCH3 Methods Intervention or Testing: Two ImageNet-pretrained VGG16 classifiers (WT vs. each mutant line) were trained with subject-grouped splitting. Grad-CAM++ and occlusion sensitivity maps defined class-discriminative regions of interest (ROIs). A centerline-based morphometry pipeline sampled luminal diameter along ordered vessel centerlines to compute mean and maximum diameter, diameter coefficient of variation, and tortuosity. A fast Fourier transform-derived vessel beading index (VBI) quantified periodic diameter oscillations using normalized, band-limited spectral power. Metrics were computed for large-vessel and small-vessel masks in both whole-field and ROI-restricted domains. Additional robustness analyses assessed hold-out testing, out-of-sample saliency, ROI-threshold sensitivity, alternative VBI spatial-period bands, and repeated balanced retraining. Main Outcome Measures: Primary biological outcomes were large-vessel mean diameter, maximum diameter, and VBI in whole-field and ROI-restricted analyses; classifier discrimination (area under the curve) was reported as supportive performance of the localization framework. Results: Whole-field morphometry detected generalized large-vessel dilation in both mutants versus WT (mean diameter: WT 27.82 μm; C455R 30.94 μm; R1031C 32.03 μm; Conclusions: Explainable deep learning-guided localization on FFA identifies disease-informative vessel segments and enables sensitive quantification of focal luminal dilation and periodic beading that are diluted by whole-field averages. This framework may support development of retinal biomarkers and longitudinal monitoring in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.