Artificial neural networks in hard tissue engineering: Another look at age-dependence of trabecular bone properties in osteoarthritis

Abstract

Artificial Neural Network (ANN) model has been developed to correlate age of severely osteoarthritic male and female specimens with key mechanical and structural characteristics of their trabecular bone. The complex interdependency between age, gender, compressive strength, porosity, morphology and level of interconnectivity was analysed in multi-dimensional space using a two-layer feedforward ANN. Trained by Levenberg-Marquardt back propagation algorithm, the ANN achieved regression factor of R = 96.3% between the predicted and target age when optimised for the experimental dataset. Results indicate a strong correlation of the 5-dimensional vector of physical properties of the bone with the age of the specimens. The inverse problem of estimating compressive strength as the key bone fracture risk was also investigated. The outcomes yield correlation between predicted and target compressive strength with the regression factor of R = 97.4%. Within the limitations of the input data set, the ANNs provide robust predictive models for hard tissue engineering decision support.