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

Abstract

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.