Healing Pattern Analysis for Dental Implants Using the Mechano-Regulatory Tissue Differentiation Model

Comparing the osteogenesis outcomes of different lumbar interbody fusions (A/O/X/T/PLIF) by evaluating their mechano-driven fusion processes

BACKGROUND

In lumbar interbody fusion (LIF), achieving proper fusion status requires osteogenesis to occur in the disc space. Current LIF techniques, including anterior, oblique, lateral, transforaminal, and posterior LIF (A/O/X/T/PLIF), may result in varying osteogenesis outcomes due to differences in biomechanical characteristics.

METHODS

A mechano-regulation algorithm was developed to predict the fusion processes of A/O/X/T/PLIF based on finite element modeling and iterative evaluations of the mechanobiological activities of mesenchymal stem cells (MSCs) and their differentiated cells (osteoblasts, chondrocytes, and fibroblasts). Fusion occurred in the grafting region, and each differentiated cell type generated the corresponding tissue proportional to its concentration. The corresponding osteogenesis volume was calculated by multiplying the osteoblast concentration by the grafting volume.

RESULTS

TLIF and ALIF achieved markedly greater osteogenesis volumes than did PLIF and O/XLIF (5.46, 5.12, 4.26, and 3.15 cm3, respectively). Grafting volume and cage size were the main factors influencing the osteogenesis outcome in patients treated with LIF. A large grafting volume allowed more osteoblasts (bone tissues) to be accommodated in the disc space. A small cage size reduced the cage/endplate ratio and therefore decreased the stiffness of the LIF. This led to a larger osteogenesis region to promote osteoblastic differentiation of MSCs and osteoblast proliferation (bone regeneration), which subsequently increased the bone fraction in the grafting space.

CONCLUSION

TLIF and ALIF produced more favorable biomechanical environments for osteogenesis than did PLIF and O/XLIF. A small cage and a large grafting volume improve osteogenesis by facilitating osteogenesis-related cell activities driven by mechanical forces.

Prediction of Bone Healing around Dental Implants in Various Boundary Conditions by Deep Learning Network

Tissue differentiation varies based on patients' conditions, such as occlusal force and bone properties. Thus, the design of the implants needs to take these conditions into account to improve osseointegration. However, the efficiency of the design procedure is typically not satisfactory and needs to be significantly improved. Thus, a deep learning network (DLN) is proposed in this study. A data-driven DLN consisting of U-net, ANN, and random forest models was implemented. It serves as a surrogate for finite element analysis and the mechano-regulation algorithm. The datasets include the history of tissue differentiation throughout 35 days with various levels of occlusal force and bone properties. The accuracy of day-by-day tissue differentiation prediction in the testing dataset was 82%, and the AUC value of the five tissue phenotypes (fibrous tissue, cartilage, immature bone, mature bone, and resorption) was above 0.86, showing a high prediction accuracy. The proposed DLN model showed the robustness for surrogating the complex, time-dependent calculations. The results can serve as a design guideline for dental implants.

Application of finite element analysis to tissue differentiation and bone remodelling approaches and their use in design optimization of orthopaedic implants: A review

Post-operative bone growth and long-term bone adaptation around the orthopaedic implants are simulated using the mechanoregulation based tissue-differentiation and adaptive bone remodelling algorithms, respectively. The primary objective of these algorithms was to assess biomechanical feasibility and reliability of orthopaedic implants. This article aims to offer a comprehensive review of the developments in mathematical models of tissue-differentiation and bone adaptation and their applications in studies involving design optimization of orthopaedic implants over three decades. Despite the different mechanoregulatory models developed, existing literature confirm that none of the models can be highly regarded or completely disregarded over each other. Not much development in mathematical formulations has been observed from the current state of knowledge due to the lack of in vivo studies involving clinically relevant animal models, which further retarded the development of such models to use in translational research at a fast pace. Future investigations involving artificial intelligence (AI), soft-computing techniques and combined tissue-differentiation and bone-adaptation studies involving animal subjects for model verification are needed to formulate more sophisticated mathematical models to enhance the accuracy of pre-clinical testing of orthopaedic implants.

Influence of sequential opening/closing of interface gaps and texture density on bone growth over macro-textured implant surfaces using FE based mechanoregulatory algorithm

Intramedullary implant fixation is achieved through a press-fit between the implant and the host bone. A stronger press-fit between the bone and the prosthesis often introduces damage to the bone canal creating micro-gaps. The aim of the present investigation is to study the influences of simultaneous opening/closing of gaps on bone growth over macro-textured implant surfaces. Models based on textures available on CORAIL and SP-CL hip stems have been considered and 3D finite element (FE) analysis has been carried out in conjunction with mechanoregulation based tissue differentiation algorithm. Additionally, using a full-factorial approach, different combinations (between 5 µm to 15 µm) of sliding and gap distances at the bone-implant interface were considered to understand their combined influences on bone growth. All designs show an elevated fibrous tissue formation (10.96% at 5 µm to 29.38% at 40 µm for CORAIL based textured model; 11.45% at 5 µm to 32.25% at 40 µm for SP-CL based textured model) and inhibition of soft cartilaginous tissue (75.64% at 5 µm to 53.94% at 40 µm for CORAIL based model; 76.02% at 5 µm to 53.60% at 40 µm SP-CL based model) at progressively higher levels of normal micromotion, leading to a fragile bone-implant interface. These results highlight the importance of minimizing both sliding and gap distances simultaneously to enhance bone growth and implant stability. Further, results from the studies with differential texture density over CORAIL based implant reveal a non-linear complex relationship between tissue growth and texture density which might be investigated in a machine learning framework.