Assessment of cortical bone fracture resistance curves by fusing artificial neural networks and linear regression. Issue 2 (25th January 2018)

• Record Type: Journal Article
• Authors: Vukicevic, Arso M.; Jovicic, Gordana R.; Jovicic, Milos N.; Milicevic, Vladimir L.; Filipovic, Nenad D.
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 21:Issue 2(2018)
• Page Start: 169
• Page End: 176
• Publication Date: 2018-01-25
• Subjects: Cortical bone -- fracture resistance curve -- artificial neural networks -- linear regression; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2018.1431220
• Languages: English
• ISSNs: 1025-5842

Abstract

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 (R 2 = 0.82 vs. R 2 = 0.76) and Toughness threshold with ageing (R 2 = 0.73 vs. R 2 = 0.66).