Format

Send to

Choose Destination
Comput Methods Biomech Biomed Engin. 2018 Feb;21(2):169-176. doi: 10.1080/10255842.2018.1431220. Epub 2018 Jan 31.

Assessment of cortical bone fracture resistance curves by fusing artificial neural networks and linear regression.

Author information

1
a Faculty of Engineering Sciences , University of Kragujevac , Kragujevac , Serbia.
2
b Research and Development Center for Bioengineering , Kragujevac , Serbia.
3
c Faculty of Information Technology , Belgrade Metropolitan University , Belgrade , Serbia.

Abstract

Bone injures (BI) represents one of the major health problems, together with cancer and cardiovascular diseases. Assessment of the risks associated with BI is nontrivial since fragility of human cortical bone is varying with age. Due to restrictions for performing experiments on humans, only a limited number of fracture resistance curves (R-curves) for particular ages have been reported in the literature. This study proposes a novel decision support system for the assessment of bone fracture resistance by fusing various artificial intelligence algorithms. The aim was to estimate the R-curve slope, toughness threshold and stress intensity factor using the two input parameters commonly available during a routine clinical examination: patients age and crack length. Using the data from the literature, the evolutionary assembled Artificial Neural Network was developed and used for the derivation of Linear regression (LR) models of R-curves for arbitrary age. Finally, by using the patient (age)-specific LR models and diagnosed crack size one could estimate the risk of bone fracture under given physiological conditions. Compared to the literature, we demonstrated improved performances for estimating nonlinear changes of R-curve slope (R2 = 0.82 vs. R2 = 0.76) and Toughness threshold with ageing (R2 = 0.73 vs. R2 = 0.66).

KEYWORDS:

Cortical bone; artificial neural networks; fracture resistance curve; linear regression

PMID:
29383945
DOI:
10.1080/10255842.2018.1431220
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Taylor & Francis
Loading ...
Support Center