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A workflow for 3D printing highly accurate, customized human skull models for anatomical education and clinical
Marina Moser1, Michel Beyer2, Magdalena Müller-Gerbl1
1Division of Anatomy, Department of Biomedicine, University of Basel, Basel, Switzerland.
Abstract:
Accurate representation of human skulls is essential for anatomy education and clinical training. Although real skulls are considered the gold standard, their use is limited by fragility, cost, ethical considerations, and restricted availability. Commercial plastic models are durable and accessible but are typically derived from standardized specimens and therefore lack anatomical variability, pathological features, and the precision required for professional education and clinical practice. This study aimed to establish and validate a workflow for producing anatomically accurate, durable, customizable, and cost-effective 3D-printed human skulls that represent clinically relevant anatomical diversity. Using CT data, one female and one male cadaveric head were selected to ensure sex-specific representation. The specimens underwent specialized anatomical preparation to optimize CT image acquisition and 3D reconstruction quality. Segmented CT datasets were converted into digital skull models and fabricated using a custom blend of commercially available printing materials, enabling accurate reproduction of fine structures and individual variation. Educational relevance was explored through a structured questionnaire completed by dentistry students (n = 30), evaluating perceived anatomical realism and the value of anatomical diversity for learning. The workflow enabled rapid and flexible production of high-fidelity skulls using accessible resources. Compared with commercial models, the custom prints preserved intricate anatomical detail and authentically depicted sex-specific traits, individual variation, and pathology. Students emphasized the educational value of incorporating anatomical diversity into training. Custom 3D-printed skulls provide a practical enhancement to existing teaching resources. The workflow is readily extensible to additional datasets, allowing progressive expansion of anatomical diversity in anatomy education.

