Determination of the mechanical and physical properties of cartilage by coupling poroelastic-based finite element models of indentation with artificial neural networks

Vahid Arbabi; Behdad Pouran; Gianni Campoli; Harrie Weinans; Amir A Zadpoor

doi:10.1016/j.jbiomech.2015.12.014

Determination of the mechanical and physical properties of cartilage by coupling poroelastic-based finite element models of indentation with artificial neural networks

J Biomech. 2016 Mar 21;49(5):631-637. doi: 10.1016/j.jbiomech.2015.12.014. Epub 2016 Feb 23.

Authors

Vahid Arbabi¹, Behdad Pouran², Gianni Campoli³, Harrie Weinans⁴, Amir A Zadpoor³

Affiliations

¹ Department of Biomechanical Engineering, Faculty of Mechanical, Maritime, and Materials Engineering, Delft University of Technology (TU Delft), Mekelweg 2, 2628CD Delft, The Netherlands. Electronic address: v.arbabi@gmail.com.
² Department of Biomechanical Engineering, Faculty of Mechanical, Maritime, and Materials Engineering, Delft University of Technology (TU Delft), Mekelweg 2, 2628CD Delft, The Netherlands; Department of Orthopedics, UMC Utrecht, Heidelberglaan 100, 3584CX Utrecht, The Netherlands.
³ Department of Biomechanical Engineering, Faculty of Mechanical, Maritime, and Materials Engineering, Delft University of Technology (TU Delft), Mekelweg 2, 2628CD Delft, The Netherlands.
⁴ Department of Biomechanical Engineering, Faculty of Mechanical, Maritime, and Materials Engineering, Delft University of Technology (TU Delft), Mekelweg 2, 2628CD Delft, The Netherlands; Department of Orthopedics, UMC Utrecht, Heidelberglaan 100, 3584CX Utrecht, The Netherlands; Department of Rheumatology, UMC Utrecht, Heidelberglaan 100, 3584CX Utrecht, The Netherlands.

PMID: 26944689
DOI: 10.1016/j.jbiomech.2015.12.014

Abstract

One of the most widely used techniques to determine the mechanical properties of cartilage is based on indentation tests and interpretation of the obtained force-time or displacement-time data. In the current computational approaches, one needs to simulate the indentation test with finite element models and use an optimization algorithm to estimate the mechanical properties of cartilage. The modeling procedure is cumbersome, and the simulations need to be repeated for every new experiment. For the first time, we propose a method for fast and accurate estimation of the mechanical and physical properties of cartilage as a poroelastic material with the aid of artificial neural networks. In our study, we used finite element models to simulate the indentation for poroelastic materials with wide combinations of mechanical and physical properties. The obtained force-time curves are then divided into three parts: the first two parts of the data is used for training and validation of an artificial neural network, while the third part is used for testing the trained network. The trained neural network receives the force-time curves as the input and provides the properties of cartilage as the output. We observed that the trained network could accurately predict the properties of cartilage within the range of properties for which it was trained. The mechanical and physical properties of cartilage could therefore be estimated very fast, since no additional finite element modeling is required once the neural network is trained. The robustness of the trained artificial neural network in determining the properties of cartilage based on noisy force-time data was assessed by introducing noise to the simulated force-time data. We found that the training procedure could be optimized so as to maximize the robustness of the neural network against noisy force-time data.

Keywords: Artificial neural networks; Cartilage mechanical and physical properties; Indentation; Poroelastic constitutive modeling.

MeSH terms

Algorithms
Cartilage, Articular*
Elasticity*
Finite Element Analysis*
Humans
Neural Networks, Computer*
Porosity
Stress, Mechanical