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

Abstract

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 C455R [C455R] n = 12; NOTCH3 R1031C [R1031C] n = 8) yielding 1670 analyzable FFA images. 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; P ≤ 0.001) with reduced tortuosity ( P P P ≤ 0.001). Occlusion ROIs showed concordant VBI increases (WT 2.11; C455R 3.84; R1031C 4.90; P 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.

Plant phenotyping relevance

FFA画像から網膜血管径・蛇行度・ビーディングを定量化する、説明可能な深層学習誘導ワークフローを開発・検証しており、マウスの植物研究ではないものの、対象が動物の網膜血管病変であるため本植物フェノタイピング索引の範囲外です。

Code and data availability

The paper's Data Availability statement explicitly deposits the model training and analysis code (VGG16 classifier, Grad-CAM++/occlusion ROI, centerline morphometry, VBI pipeline) in a public GitHub repository matching an allowed URL. Raw FFA images are only available upon request, so no public image/phenotype dataset.