“…With the three-dimensional callus model, we can obtained an intact information of spatial and temporal distribution for biophysical stimulus, cartilage concentration, bone concentration and blood perfusion. When compared with Wang’s model 9 , we can concluded that in Case B (Fig. 4b ), our model curve is closer to the experimental curve between days 21 and 49, which is the most obvious time for fracture healing.…”
Section: Discussionsupporting
confidence: 62%
“… Figure 4 Comparison of IFM between average values of experimental data, Wang’s simulated results and our simulated results. Figure 5 Comparison of computational time between Wang’s model 9 and our model for both cases A and B. …”
Section: Resultsmentioning
confidence: 99%
“…Single finite element models and biphasic finite element models were the two primary types used to calculate the mechanical stimuli. In single finite element models 3 – 9 , tissue differentiation pathways were regulated by mechanical stimuli (Fig. 1a ) based on the work of Claes and Heigele 10 and fuzzy logic control was used to simulate the process of tissue differentiation within the callus region, which is an easy way to establish the healing process as linguistic principles.…”
Section: Introductionmentioning
confidence: 99%
“… 29 and computational simulation conducted by Wang et al . 9 , which simulated the fracture healing process with linear elastic analysis and fuzzy logic. Through comparison, we can conclude that under the condition of large interfragmentary movement (IFM), the predicted IFM curve of our model is closer to the average experimental data.…”
A dynamic model regulated by both biphasic poroelastic finite element analysis and fuzzy logic control was established. Fuzzy logic control was an easy and comprehensive way to simulate the tissue differentiation process, and it is convenient for researchers and medical experts to communicate with one another to change the fuzzy logic rules and improve the simulation of the tissue differentiation process. In this study, a three-dimensional fracture healing model with two different interfragmentary movements (case A: 0.25 mm and case B: 1.25 mm) was analysed with the new set-up computational model. As the healing process proceeded, both simulated interfragmentary movements predicted a decrease and the time that the decrease started for case B was later than that for case A. Compared with experimental results, both cases corresponded with experimental data well. The newly established dynamic model can simulate the healing process under different mechanical environments and has the potential to extend to the multiscale healing model, which is essential for reducing the animal experiments and helping to characterise the complex dynamic interaction between tissue differentiations within the callus region.
“…With the three-dimensional callus model, we can obtained an intact information of spatial and temporal distribution for biophysical stimulus, cartilage concentration, bone concentration and blood perfusion. When compared with Wang’s model 9 , we can concluded that in Case B (Fig. 4b ), our model curve is closer to the experimental curve between days 21 and 49, which is the most obvious time for fracture healing.…”
Section: Discussionsupporting
confidence: 62%
“… Figure 4 Comparison of IFM between average values of experimental data, Wang’s simulated results and our simulated results. Figure 5 Comparison of computational time between Wang’s model 9 and our model for both cases A and B. …”
Section: Resultsmentioning
confidence: 99%
“…Single finite element models and biphasic finite element models were the two primary types used to calculate the mechanical stimuli. In single finite element models 3 – 9 , tissue differentiation pathways were regulated by mechanical stimuli (Fig. 1a ) based on the work of Claes and Heigele 10 and fuzzy logic control was used to simulate the process of tissue differentiation within the callus region, which is an easy way to establish the healing process as linguistic principles.…”
Section: Introductionmentioning
confidence: 99%
“… 29 and computational simulation conducted by Wang et al . 9 , which simulated the fracture healing process with linear elastic analysis and fuzzy logic. Through comparison, we can conclude that under the condition of large interfragmentary movement (IFM), the predicted IFM curve of our model is closer to the average experimental data.…”
A dynamic model regulated by both biphasic poroelastic finite element analysis and fuzzy logic control was established. Fuzzy logic control was an easy and comprehensive way to simulate the tissue differentiation process, and it is convenient for researchers and medical experts to communicate with one another to change the fuzzy logic rules and improve the simulation of the tissue differentiation process. In this study, a three-dimensional fracture healing model with two different interfragmentary movements (case A: 0.25 mm and case B: 1.25 mm) was analysed with the new set-up computational model. As the healing process proceeded, both simulated interfragmentary movements predicted a decrease and the time that the decrease started for case B was later than that for case A. Compared with experimental results, both cases corresponded with experimental data well. The newly established dynamic model can simulate the healing process under different mechanical environments and has the potential to extend to the multiscale healing model, which is essential for reducing the animal experiments and helping to characterise the complex dynamic interaction between tissue differentiations within the callus region.
“…7,8 In 2016, Monan Wang et al extended Simon's model and built a threedimensional fracture geometry model, in which the effects of mechanical environment and blood supply on fracture healing were simulated. 9 In 2012, Burke and Kelly proposed a mathematical simulation model of mesenchymal stem cell differentiation regulated by foundation stiffness and oxygen tension during fracture healing. 10 Geris et al described the temporal and spatial variation of bone regeneration by 12 continuous variables (mesenchymal stem cells, cartilage cells, osteoblasts, fibroblasts, endothelial cells, cartilage matrix, bone matrix, fibrous matrix, vascular matrix, osteogenic growth factor, cartilage growth factor and vascular growth factor).…”
According to the mechanical conditions of fracture fixation and the oxygen levels in the tissues, a simulation model of fracture healing process was built to describe the relationship among mechanical stability, oxygen levels in tissues and tissue differentiation during the second fracture healing. Different from the previous simulation model, in this paper, we took the threedimensional model as the research object, solved the mechanical stimulation by finite element method, established the partial differential equation to solve the spatial and temporal variation of the oxygen in tissues. The process of tissue differentiation was described by fuzzy control method. The initial stage of fracture healing, intramembranous ossification, chondrogenesis, cartilage calcification and endochondral ossification during the fracture healing process were simulated, and the properties of tissue materials were continuously updated to complete the iterative process. The simulation program of fracture healing process was independently developed in Eclipse environment, and the simulation results were compared with experimental data and those of other fracture healing simulation models to verify the simulation program in this paper. Finally, the processes of transverse fracture healing in rats with different axial stability under normoxic, hypoxic and hyperoxic conditions was simulated, and the effects of different tissue oxygen levels and interosseous stabilities on fracture healing were analyzed. It is concluded by simulation that the delayed healing or non-union of bone will occur when in state of tissue hypoxia or interosseous instability, normal healing will occur when in state of tissue normoxia, and the healing will be accelerated when in state of tissue hyperoxia.
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