Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/BHI.2014.6864441 | Biomedical and Health Informatics |
Keywords | Field | DocType |
backpropagation,biomechanics,bone,compressive strength,diseases,feedforward neural nets,fracture,injuries,inverse problems,medical computing,orthopaedics,physiological models,porosity,tissue engineering,5-dimensional vector,Artificial Neural Network model,Levenberg-Marquardt back propagation algorithm,age-dependence,complex interdependency,experimental dataset,gender,hard tissue engineering decision support,input data set,interconnectivity level,inverse problem,key bone fracture risk,mechanical characteristics,morphology,multidimensional space,osteoarthritis,physical properties,porosity,predicted age,predicted compressive strength,predictive model,regression factor,severely osteoarthritic female specimens,severely osteoarthritic male specimens,specimen age,structural characteristics,target age,target compressive strength,trabecular bone properties,two-layer feedforward ANN | Pattern recognition,Regression,Bone fracture,Compressive strength,Hard tissue,Osteoarthritis,Correlation,Inverse problem,Artificial intelligence,Artificial neural network,Mathematics | Conference |
Citations | PageRank | References |
1 | 0.41 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Torgyn Shaikhina | 1 | 16 | 1.59 |
| N. A. Khovanova | 2 | 23 | 2.72 |
| Kajal K. Mallick | 3 | 1 | 0.41 |