A Macro-Micro FE and Machine Learning Based Design of Diamond Lattice Tibial Implant to Improve Biomechanical and Osseointegration Performance.

Poor long-term implant-bone fixation remains a significant clinical problem for Total Ankle Replacement (TAR). Recent advancements in metal additive manufacturing technology facilitate the development of porous lattice-structured implants. However, the optimal porous structure parameter, especially porosity, for the designing of porous implants for TAR remains ambiguous. The objective of the present study is to design porous diamond-structured tibial implant for TAR using macro-micro FE and Machine Learning (ML) based approach to enhance biomechanical and osseointegration performance. Four porous diamond architectures were designed with porosity varying from 50% to 80%. The study entails the macro-microscale FE modelling of four different porous diamond structured tibial implants (PDSTI) to assess the biomechanical performance and to perform bone ingrowth simulations using a physics based progressive mechanoregulatory tissue differentiation algorithm. ML models were used to predict the value of site-specific bone ingrowth, and the average value was compared between these PDSTI. ML models were accurately able to predict the site-specific bone ingrowth for each PDSTI. Bone ingrowth results revealed that the PDSTI at 70% porosity is more conducive to bone formation as well as better load transfer (less stress shielding) to the bone in comparison to a traditional solid implant. The lowest value of bone ingrowth was noted for the PDSTI at 80% porosity, indicating that large porosity is not suitable for bone ingrowth. The findings ultimately contribute to improving the clinical outcomes for TAR by reducing the risk of aseptic loosening.