Deep learning-based prediction of cerebral white matter hyperintensity burden using carotid magnetic resonance angiography

Purpose: White matter hyperintensities (WMHs) are key neuroimaging markers of cerebral small vessel disease (cSVD), associated with cognitive decline and increased stroke risk. We aimed to investigate whether carotid time-of-flight (TOF) magnetic resonance angiography (MRA), a routinely acquired and non-invasive vascular imaging modality, can be utilized to independently predict WMH burden using deep learning. Methods: We developed a deep learning-based framework to predict WMH presence and severity using only 3D carotid TOF MRA. Two classification tasks were defined: binary (grade 0 vs. grades 1–3) and three-class (grade 0, 1, 2–3) classification. Four model architectures— simple fully convolutional network (SFCN), ResNet10, MedicalNet, and Medical Slice Transformer—were evaluated. To enhance model interpretability, we performed saliency mapping and occlusion analysis. Results: SFCN performed the best, achieving an accuracy of 76.5% and an area under the receiver operating characteristic curve (AUC) of 0.874 in binary classification, along with a 63.5% accuracy and a 0.827 AUC in WMH severity classification. Interpretability analyses confirmed that models predominantly focused on carotid vessel regions, which supports known vascular associations with WMH burden. Conclusion: Carotid TOF MRA alone can serve as a predictive marker for WMH burden when analyzed using deep learning. This approach highlights the potential utility of extracranial carotid imaging as a non-invasive surrogate for early and accessible assessment of cerebrovascular risk.