Harnessing generative design algorithms in cranioplasty

Abstract

Cranioplasty, which is essential for restoring craniofacial functions and aesthetics, has undergone a continuous process of evolution, incorporating increasingly precise and personalized treatments. Cranial patient-specific implants (PSIs) produced by molding or additive manufacturing (AM) represent a notable example of the implementation of tailored patient care pathways. Nevertheless, these tailor-made workflows still necessitate input from the user and must be further automated for their optimal integration at the point of care (POC). This study examines the utilization of generative design algorithms to develop cranial PSIs that are generated autonomously and three-dimensionally (3D) printed using stereolithography (SLA) technology. In this study, three specimens of a PSI mold were generated using the software nTopology and printed with a Form 3B SLA printer. The dimensional accuracy of the molds was validated both before and after sterilization, demonstrating minimal deviations within the acceptable clinical ranges. The generative design algorithm reduced design time from 2 hours to 1.3 minutes and minimized manual labor, yielding high-fidelity implants. The workflow demonstrated superior accuracy and efficiency compared to traditional silicone molds, facilitating easy implant release. The results suggest that this automated 3D-printed mold approach offers a viable, efficient alternative for silicon mold-based cranial PSI manufacturing, pending further clinical validation and regulatory compliance.