Beyond visual inspection: the deep learning revolution in quantitative cerebrovascular imaging

The rising global burden of cerebrovascular disease, propelled by an aging population, highlights the inherent limitations of conventional, labor-intensive diagnostic paradigms. In the context of time-sensitive stroke management, variability in image interpretation and the high rate of misclassification, particularly during the assessment of transient ischemic attack (TIA), underscore the urgent need for more consistent and efficient diagnostic solutions. Artificial intelligence (AI), particularly deep learning (DL), offers a transformative pathway by automating the analysis of complex neurovascular imaging. Here, we conduct a comprehensive examination of how DL is revolutionizing stroke-related image analysis, moving beyond general assertions of potential to discuss specific technical implementations. We systematically detail the evolution from traditional segmentation algorithms to advanced deep learning architectures, such as U-Net, DeepMedic, and their variants, in performing critical tasks. These tasks encompass the automated segmentation of intracranial and extracranial (carotid) arteries, the quantification of stenosis and plaque burden, and the hemodynamic assessment of vascular lesions across modalities including MRA, CTA, and DSA. By synthesizing landmark studies, our analysis delineates three core aspects: the technological trajectory of DL models in achieving expert-level accuracy in vascular feature extraction in controlled studies; the clinical translation of these tools into diagnostic, prognostic, or therapeutic procedural planning workflows; and the persistent challenges and future directions, including data standardization, model generalizability, and multimodal integration. This review posits that DL represents not merely an assistive technology but a foundational cornerstone for the next generation of precision cerebrovascular medicine. It holds the potential to bridge critical gaps in diagnostic speed, objectivity, and accessibility, provided its development and validation are guided by rigorous, interdisciplinary collaboration.