Design of Porous Structures for Bone Regeneration Using a Machine Learning Model & Computational Mechanical Simulation
DOI:
https://doi.org/10.70567/mc.v41i17.90Keywords:
Bone implants, porous structures, bone regeneration, 3D printing, machine learning, computational mechanical simulationAbstract
Recent addition of 3D printing into manufacturing processes have revolutionized regenerative medicine in traumatology (RMT) by enabling the production of implants with porous structures to promote bone regeneration (BR). Two key features of these structures were identified: excellent mechanical properties and biocompatibility. However, diverse results have been reported regarding the optimal morphological characteristics of geometric variants to stimulate BR. This work proposes a novel methodology for designing bone implants with optimized porous structures, by combining Machine Learning with Computational Mechanical Simulation. A literature review led to the creation of a database of 100 3D porous structures with proven potential to stimulate BR. A Statistical Shape Model was trained using this database to capture the morphological characteristics of each geometry and generate a library of novel porous structures. Those structures were evaluated using CMS, employing the Finite Element Method, and the geometries with the best mechanical performance were selected for a potential biological response. The optimized structures obtained set the stage for in vivo validation to promote BR, with a potential impact on RMT.
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