Intelligent cerebrovascular intervention: a deep learning approach to multimodal image processing, hemodynamic modeling, and 3D printing

Cerebrovascular diseases, particularly intracranial aneurysms, represent a leading cause of mortality and long-term disability worldwide. The integration of deep learning with multimodal imaging, hemodynamic modeling, and 3D printing offers transformative potential for personalized neurosurgical planning. However, the field lacks a comprehensive synthesis of methodological approaches, validation frameworks, and clinical translation pathways. This research provides a comprehensive analysis of deep learning-based approaches for cerebrovascular image processing, hemodynamic modeling, and patient-specific 3D printing, with emphasis on multimodal integration, methodological innovations, and pathways to clinical implementation. Following PRISMA guidelines, we systematically searched PubMed, IEEE Xplore, Scopus, and Web of Science for peer-reviewed studies published between 2015 and 2026. Studies were included if they employed deep learning for cerebrovascular image segmentation, multimodal fusion, hemodynamic prediction, or 3D printing applications. Quality assessment was performed using tailored instruments for each methodological domain. From 1,847 initial records, 127 studies met inclusion criteria. Key findings include: (1) 3D U-Net architectures with attention mechanisms achieve state-of-the-art segmentation performance (Dice coefficients: 0.85–0.92) for major cerebral vessels, though performance degrades significantly for vessels < 0.5 mm; (2) Multimodal fusion strategies have evolved from early/late fusion to hybrid architectures incorporating transformers and cross-attention mechanisms, improving fusion fidelity by 15–25% over unimodal approaches; (3) Reduced-order modeling combining Proper Orthogonal Decomposition with LSTM networks enables real-time hemodynamic prediction while preserving > 99% of flow energy with < 10 modes; (4) Biomechanical characterization reveals that silicone elastomers more closely match native vascular tissue (elastic modulus: 0.02–1.16 N/mm²) than current 3D-printed materials (0.85–1.02 N/mm²), though both fail to replicate nonlinear tissue behavior; (5) Explainable AI methods (Grad-CAM, SHAP, LIME) are increasingly employed but lack standardized validation protocols and clinical expert involvement in evaluation. Deep learning-based multimodal integration has demonstrated significant potential for advancing cerebrovascular surgical planning, but critical gaps remain in small vessel segmentation, biomechanical material fidelity, standardized validation frameworks, and prospective clinical validation. Future research should prioritize foundation model development, physics-informed neural networks, multi-material 3D printing with programmable properties, and human-centered explainability evaluation to enable clinical translation.