Objective: To model the relationship between stimulating stress and fracture strength using BP neural networks, and to provide a theoretical basis for accurate prediction of the rate of fracture healing.
Methods: The bilateral tibiae in New Zealand rabbits were osteotomized and fixed by stress-relaxation plate(SRP) and rigid plate(RP), respectively. The stress shielding rate and bending strength of the healing fractures were measured at 2 to 48 weeks postoperatively. A BP neural network was constructed and trained using the experimental data of the stress-relaxation group. Then the trained network was used for simulation to predict fracture strength of the two groups from the stress at the fracture site.
Results: With the input of the data that has been used to train the network, fracture strength similar to those measured in experiment was calculated from the BP neural network. However, poor results were obtained with the input of new data.
Conclusion: BP neural network can be used to investigate the influence of various factors on fracture healing quantitatively, and to predict the rate of healing. However, the model still needs to be perfected. More experimental or clinical data are needed to train the network