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Beyond visual inspection: the deep learning revolution in quantitative cerebrovascular imaging
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DOI：10.3389/fnins.2025.1768107.png)
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Abstract
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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.
Keywords:
deep learning
image segmentation
quantitative imaging
cerebrovascular imaging
stroke diagnosis
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