Artificial Intelligence and Digital Workflow in Craniofacial Bone Tissue Engineering: From Cone-Beam Computed Tomography (CBCT) to Personalized Bioceramic Implants.

Background

Reconstruction of craniofacial bone defects caused by tumor removal, infection, congenital anomalies, or trauma remains a major challenge. Recent advances in artificial intelligence (AI), digital imaging, and additive manufacturing have enabled personalized treatment strategies. This review discusses AI-driven workflows from cone-beam computed tomography (CBCT) image acquisition to implant fabrication using patient-specific bioceramics.

Methods

Several electronic databases were searched, including Scopus, PubMed, ScienceDirect, and Web of Science. A peer-reviewed article published between 2015 and 2026 was considered for inclusion. This review provides an overview of AI applications in craniofacial tissue engineering, including CBCT image segmentation, three-dimensional reconstruction, virtual surgical planning, computer-aided design/computer-aided manufacturing (CAD/CAM), topology optimization, finite element analysis, and three-dimensional bioceramic scaffold printing.

Results

AI improves accuracy, efficiency, and reproducibility of craniofacial reconstructions when used in conjunction with AI-assisted workflows. Through automated image segmentation and anatomical modeling, operator dependence can be reduced, and predictive algorithms can optimize biomechanical properties. CAD/CAM and 3D printing are used to manufacture bioceramic implants with controlled porosity, mechanical integrity, and enhanced osteoconductive properties. AI-driven predictive models have demonstrated potential for supporting material selection, manufacturing quality control, and treatment planning; however, their ability to reliably predict long-term postoperative outcomes requires further validation through prospective clinical studies. Data standardization, algorithm transparency, regulatory approval, clinical trial validation, and ethical considerations remain challenges.

Conclusions

AI, CBCT imaging, computational modeling, and advanced bioceramic manufacturing are being combined to create an end-to-end digital workflow for personalized reconstruction. Clinical studies must be conducted prospectively to maximize the therapeutic potential of these technologies.