Restricted accessResearch articleFirst published online 2026
Mechanobiological design strategy for additively manufacturable tibial ankle implant for enhanced biomechanical and osseointegration performance: A finite element and machine learning approach
The leading cause of revision surgeries in ankle arthroplasty is aseptic loosening of the tibial implant, resulting from adverse bone remodelling and insufficient osseointegration. Aseptic loosening depends on multiple factors, such as the design of the implant, the porous surface of the implant, the quality of bone, implant positioning, wear debris, etc. The extent to which the design of porous architecture and its relationship with aseptic loosening failure mechanisms remains unexplored. The study aims to identify the lattice design of porous rhombic dodecahedron architecture of tibial implants that would be able to maximise bone formation and reduce stress shielding using macro-micro finite element (FE) analysis with machine learning (ML) approach. The study entails the macro-microscale FE modelling of four porous rhombic dodecahedron tibial implants (PRDTI), referred as PRDTI50, PRDTI60, PRDTI70, and PRDTI80. Based on macro-micro-FE determined dataset, four artificial neural network (ANN)-based ML algorithms were trained and validated for faster prediction of bone ingrowth. Results evidenced that von Mises stress in the tibia exhibited elevated stresses for PRDTI80 and PRDTI70 implants compared to PRDTI60, PRDTI50, and solid implant. Bone ingrowth results indicated that the PRDTI70 implant exhibited higher amounts of bone formation. The study proposes the PRDTI70 implant is a viable option for designing tibial implants to simultaneously reduce stress shielding and maximises bone ingrowth. This preclinical analysis sheds light on the role of porous structure design in bone formation for the development of porous tibial prostheses for TAR to prevent revision instances.
BianchiAMartinelliNSartorelliE, et al. The Bologna-Oxford total ankle replacement: a mid-term follow-up study. J Bone Jt Surg - Ser B2012; 94: 793–798. https://doi.org/10.1302/0301-620X.94B6.28283
2.
GianniniSRomagnoliMBarbadoroP, et al. Results at a minimum follow-up of 5 years of a ligaments-compatible total ankle replacement design. Foot Ankle Surg2017; 23: 116–121. https://doi.org/10.1016/j.fas.2017.03.009
3.
NajefiAMalhotraKChanO, et al. The Bologna-Oxford ankle replacement: a case series of clinical and radiological outcomes. Int Orthop2019; 43: 2333–2339. https://doi.org/10.1007/s00264-019-04362-6
4.
BianchiAMartinelliNCaboniE, et al. Long-term follow-up of Bologna-Oxford (BOX) total ankle arthroplasty. Int Orthop2021; 45(5): 1223–1231. https://doi.org/10.1007/s00264-021-05033-1
5.
MondalSGhoshR. Experimental and finite element investigation of total ankle replacement: a review of literature and recommendations. J Orthop2020; 18: 41–49. https://doi.org/10.1016/j.jor.2019.09.019
6.
ArabnejadSBurnett JohnstonRPuraJA, et al. High-strength porous biomaterials for bone replacement: a strategy to assess the interplay between cell morphology, mechanical properties, bone ingrowth and manufacturing constraints. Acta Biomater2016; 30: 345–356.
YuHXuMDuanQ, et al. 3D-printed porous tantalum artificial bone scaffolds: fabrication, properties, and applications. Biomed Mater2024; 19(4): 042002. https://doi.org/10.1088/1748-605X/ad46d2
13.
GaoHYangJJinX, et al. Porous tantalum scaffolds: fabrication, structure, properties, and orthopedic applications. Mater Des2021; 210: 110095. https://doi.org/10.1016/j.matdes.2021.110095
14.
PuthumanapullyPKBrowneM. Tissue differentiation around a short stemmed metaphyseal loading implant employing a modified mechanoregulatory algorithm: a finite element study. J Orthop Res2011; 29: 787–794.
15.
SimmonsCAMeguidSAPilliarRM. Differences in osseointegration rate due to implant surface geometry can be explained by local tissue strains. J Orthop Res2001; 19: 187–194.
16.
HollisterSJGuldbergREKuelskeCL, et al. Relative effects of wound healing and mechanical stimulus on early bone response to porous-coated implants. J Orthop Res1996; 14: 654–662.
17.
LacroixDPrendergastPJ. Three-dimensional simulation of fracture repair in the human tibia. Comput Methods Biomech Biomed Eng2002; 5: 369–376.
18.
RenTDaileyHL. Mechanoregulation modelling of bone healing in realistic fracture geometries. Biomech Model Mechanobiol2020; 19: 2307–2322.
19.
FuRBertrandDWangJ, et al. In vivo and in silico monitoring bone regeneration during distraction osteogenesis of the mouse femur. Comput Methods Programs Biomed2022; 216: 106679.
20.
ByrneDPLacroixDPlanellJA, et al. Simulation of tissue differentiation in a scaffold as a function of porosity, Young’s modulus and dissolution rate: application of mechanobiological models in tissue engineering. Biomaterials2007; 28: 5544–5554.
21.
Perier-MetzCDudaGNChecaS. Initial mechanical conditions within an optimized bone scaffold do not ensure bone regeneration - an in-silico analysis. Biomech Model Mechanobiol2021; 20: 1723–1731.
22.
VaianiLUvaAEBoccaccioA. Structural and topological design of conformal bilayered scaffolds for bone tissue engineering. Thin-Walled Struct2023; 192: 111209.
23.
MukherjeeKGuptaS. Bone ingrowth around porous-coated acetabular implant: a three-dimensional finite element study using mechanoregulatory algorithm. Biomech Model Mechanobiol2016; 15: 389–403.
24.
AndreykivAvan KeulenFPrendergastPJ. Computational mechanobiology to study the effect of surface geometry on peri-implant tissue differentiation. J Biomech Eng2008; 130: 1–11.
25.
WuCWanBEntezariA, et al. Machine learning-based design for additive manufacturing in biomedical engineering. Int J Mech Sci2024; 266: 108828.
26.
WauthleRvan der StokJAmin YavariS, et al. Additively manufactured porous tantalum implants. Acta Biomater2015; 14: 217–225.
27.
MaoSLiuYWangF, et al. Design and biomechanical analysis of patient specific porous tantalum prostheses for knee joint revision surgery. Int J Bioprinting2023; 9(4): 735. https://doi.org/10.18063/ijb.735
28.
JohnsonJEClarkeGAde Cesar NettoC, et al. Influence of sidewall retention and interference fit in total ankle replacement on implant-bone micromotion: a finite element study. J Orthop Res2024; 42: 1536–1544.
29.
Quevedo GonzálezFJSteinemanBDSturnickDR, et al. Biomechanical evaluation of total ankle arthroplasty. Part II: influence of loading and fixation design on tibial bone-implant interaction. J Orthop Res2021; 39: 103–111.
30.
ReznikovNChaseHBen ZviY, et al. Inter-trabecular angle: a parameter of trabecular bone architecture in the human proximal femur that reveals underlying topological motifs. Acta Biomater2016; 44: 65–72.
31.
LevineBRSporerSPoggieRA, et al. Experimental and clinical performance of porous tantalum in orthopedic surgery. Biomaterials2006; 27: 4671–4681.
32.
JettéBBrailovskiVDumasM, et al. Femoral stem incorporating a diamond cubic lattice structure: design, manufacture and testing. J Mech Behav Biomed Mater2018; 77: 58–72.
33.
MaconachieTLearyMLozanovskiB, et al. SLM lattice structures: properties, performance, applications and challenges. Mater Des2019; 183: 108137. https://doi.org/10.1016/j.matdes.2019.108137
34.
Rodríguez-MontañoÓLCortés-RodríguezCJUvaAE, et al. Comparison of the mechanobiological performance of bone tissue scaffolds based on different unit cell geometries. J Mech Behav Biomed Mater2018; 83: 28–45.
35.
WangCSunBZhangY, et al. Design of a novel trabecular acetabular cup and selective laser melting fabrication. Materials2022; 15(17): 6142. https://doi.org/10.3390/ma15176142
36.
MondalSGhoshR. Effects of implant orientation and implant material on tibia bone strain, implant–bone micromotion, contact pressure, and wear depth due to total ankle replacement. Proc Inst Mech Eng Part H J Eng Med2019; 233: 318–331.
37.
MinkuMukherjeeKGhoshR. Assessment of bone ingrowth around beaded coated tibial implant for total ankle replacement using mechanoregulatory algorithm. Comput Biol Med2024; 175: 108551.
38.
VargheseBShortDPenmetsaR, et al. Computed-tomography-based finite-element models of long bones can accurately capture strain response to bending and torsion. J Biomech2011; 44: 1374–1379.
39.
JainTJyotiMinku, et al. Design of functionally graded porous lattice structure tibial implant for TAR. Int J Mech Sci2024; 281: 109671.
40.
TerrierALarreaXGuerdatJ, et al. Development and experimental validation of a finite element model of total ankle replacement. J Biomech2014; 47: 742–745.
41.
YuJZhangCChenWM, et al. Finite-element analysis of the influence of tibial implant fixation design of total ankle replacement on bone–implant interfacial biomechanical performance. J Orthop Surg2020; 28: 1–10.
42.
ZhangYChenZZhaoH, et al. Comparison of joint load, motions and contact stress and bone-implant interface micromotion of three implant designs for total ankle arthroplasty. Comput Methods Programs Biomed2022; 223: 106976.
43.
UpadhyayGJyotiMinku, et al. A finite element analysis to assess tibial bone fracture in malleolar regions following total ankle replacement: influences of implant designs and implant-bone interfacial conditions. Foot Ankle Surg2025; 31(5): 418–425. https://doi.org/10.1016/j.fas.2025.01.003
44.
MinkuJainTGhoshR. Comparative analysis of tissue ingrowth in printable porous lattice structured implants: an in-silico study. Mater2024; 37: 102207.
45.
ClaesLEHeigeleCA. Magnitudes of local stress and strain along bony surfaces predict the course and type of fracture healing. J Biomech1999; 32(3): 255–266. https://doi.org/10.1016/s0021-9290(98)00153-5
46.
CampoliGWeinansHZadpoorAA. Computational load estimation of the femur. J Mech Behav Biomed Mater2012; 10: 108–119.
47.
GarijoNMartínezJGarcía-AznarJM, et al. Computational evaluation of different numerical tools for the prediction of proximal femur loads from bone morphology. Comput Methods Appl Mech Eng2014; 268: 437–450.
48.
MinkuGhoshR. A macro–micro-FE and ANN framework to assess site-specific bone ingrowth around the porous beaded-coated implant: an example with BOX® tibial implant for total ankle replacement. Med Biol Eng Comput2024; 62(6): 1639–1654. https://doi.org/10.1007/s11517-024-03034-x
49.
LiJChenDZhangY, et al. Diagonal-symmetrical and midline-symmetrical unit cells with same porosity for bone implant: mechanical properties evaluation. J Bionic Eng2019; 16: 468–479.
50.
KellyCNWangTCrowleyJ, et al. High-strength, porous additively manufactured implants with optimized mechanical osseointegration. Biomaterials2021; 279: 121206.
51.
TaniguchiNFujibayashiSTakemotoM, et al. Effect of pore size on bone ingrowth into porous titanium implants fabricated by additive manufacturing: an in vivo experiment. Mater Sci Eng C2016; 59: 690–701.
52.
ZadpoorAA. Bone tissue regeneration: the role of scaffold geometry. Biomater Sci2015; 3: 231–245.
53.
JinJWangDQianH, et al. Precision pore structure optimization of additive manufacturing porous tantalum scaffolds for bone regeneration: a proof-of-concept study. Biomaterials2025; 313: 122756. https://doi.org/10.1016/j.biomaterials.2024.122756
54.
JiaoJHongQZhangD, et al. Influence of porosity on osteogenesis, bone growth and osteointegration in trabecular tantalum scaffolds fabricated by additive manufacturing. Front Bioeng Biotechnol2023; 11: 1–16.
55.
LuoCWangCWuX, et al. Influence of porous tantalum scaffold pore size on osteogenesis and osteointegration: a comprehensive study based on 3D-printing technology. Mater Sci Eng C2021; 129: 112382.
56.
CowinSC. Wolff’s law of trabecular architecture at remodelling equilibrium. J Biomech Eng1986; 108: 83–88. https://doi.org/10.1115/1.3138584
57.
WronskiSWitATarasiukJ, et al. The impact of the parameters of the constitutive model on the distribution of strain in the femoral head. Biomech Model Mechanobiol2023; 22: 739–759.
58.
Phillips AndrewTM. Structural optimisation: biomechanics of the femur. Proc Inst Civ Eng Eng Comput Mech2012; 165: 147–154.
59.
MathaiBGuptaS. Bone ingrowth around an uncemented femoral implant using mechanoregulatory algorithm: a multiscale finite element analysis. J Biomech Eng2022; 144(2): 021004. https://doi.org/10.1115/1.4052227
60.
GhoshRChandaSChakrabortyD. The influence of macro-textural designs over implant surface on bone on-growth: a computational mechanobiology based study. Comput Biol Med2020; 124: 103937.
61.
CarpenterRDKlosterhoffBSTorstrickFB, et al. Effect of porous orthopaedic implant material and structure on load sharing with simulated bone ingrowth: a finite element analysis comparing titanium and PEEK. J Mech Behav Biomed Mater2018; 80: 68–76.
62.
AndreykivAPrendergastPJvan KeulenF, et al. Bone ingrowth simulation for a concept glenoid component design. J Biomech2005; 38: 1023–1033.
