AUTHOR=Coato Damiano , Dolino Gianmarco , Berardo Alice , Belluzzi Elisa , Pozzuoli Assunta , Ruggieri Pietro , Carniel Emanuele Luigi , Gargiulo Paolo 

TITLE=Synthetic 3D printed tibial plateau with gradient material properties for biomechanical accuracy

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1707380

DOI=10.3389/fbioe.2025.1707380

ISSN=2296-4185

ABSTRACT=IntroductionThis study presents the design and fabrication of a synthetic 3D printed tibial plateau, complete with tibial cartilages, developed to replicate the mechanical behavior of its natural counterpart.MethodsPatient-specific anatomical data were used to design the model, which was fabricated using advanced PolyJet™ multi-material printing. Gradient material properties were integrated within the construct to reproduce the stiffness variations observed in native cartilage. Three different material mixes were developed and tested under indentation loading, and the optimal configuration (Mix 3) was selected based on its mechanical fidelity to biological tissue.ResultsMix 3 successfully reproduced the regional stiffness variations of native tibial cartilage. The instantaneous modulus (IM) of the synthetic cartilage closely matched that of the biological sample, with values of 3.19 ± 1.95 MPa vs. 3.31 ± 2.33 MPa in the lateral compartment and 3.71 ± 1.38 MPa vs. 3.72 ± 2.56 MPa in the medial compartment. Statistical analysis confirmed that most regional comparisons showed no significant differences (p > 0.05), supporting the strong mechanical agreement between synthetic and native cartilage.ConclusionThis study demonstrates the potential of Digital Anatomy materials produced with PolyJet™ technology as a viable method for 3D printing anatomically and mechanically accurate models of the human tibial plateau. Overall, this approach provides a reproducible and ethically sustainable alternative to biological specimens, with implications for preclinical testing, implant design optimization, and the advancement of high-fidelity surgical training models.