Prediction of load in a long bone using an artificial neural network prediction algorithm.
Abstract: The hierarchical nature of bone makes it a difficult material to fully comprehend. The equine third metacarpal (MC3) bone experiences nonuniform surface strains, which are a measure of displacement induced by loads. This paper investigates the use of an artificial neural network expert system to quantify MC3 bone loading. Previous studies focused on determining the response of bone using load, bone geometry, mechanical properties, and constraints as input parameters. This is referred to as a forward problem and is generally solved using numerical techniques such as finite element analysis (FEA). Conversely, an inverse problem has to be solved to quantify load from the measurements of strain and displacement. Commercially available FEA packages, without manipulating their underlying algebraic formulae, are incapable of completing a solution to the inverse problem. In this study, an artificial neural network (ANN) was employed to quantify the load required to produce the MC3 displacement and surface strains determined experimentally. Nine hydrated MC3 bones from thoroughbred horses were loaded in compression in an MTS machine. Ex-vivo experiments measured strain readings from one three-gauge rosette and three distinct single-element gauges at different locations on the MC3 midshaft, associated displacement, and load exposure time. Horse age and bone side (left or right limb) were also recorded for each MC3 bone. This information was used to construct input variables for the ANN model. The ability of this expert system to predict the MC3 loading was investigated. The ANN prediction offered excellent reliability for the prediction of load in the MC3 bones investigated, i.e. R ≥ 0.98.
Copyright © 2019 Elsevier Ltd. All rights reserved.
Publication Date: 2019-11-11 PubMed ID: 31879267DOI: 10.1016/j.jmbbm.2019.103527Google Scholar: Lookup
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- Journal Article
Summary
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The research paper addresses a novel application of artificial neural network algorithms to estimate the amount of load (force) experienced by a specific type of horse bone, the metacarpal (MC3), based on measurements of strain and displacement taken during experiments. The study found that this artificial neural network method was very accurate in its predictions.
Background and Aims
- Bone, due to its hierarchical structure, is difficult to understand entirely. The MC3 bone in horses experiences non-uniform surface strains, representing the shifts induced by loads.
- Prior studies typically determined bone response using load conditions, bone construction, mechanical properties, and imposed constraints, known as a forward problem. To figure out load from strain and displacement measurements, however, is called an inverse problem.
- The intention of this study was to use artificial neural networks (ANNs—a type of machine learning algorithm) to estimate the load amounts that the MC3 bone experienced during experimental procedures.
Methods
- Nine MC3 bones from thoroughbred horses were subjected to varying amounts of compressive force in an MTS machine in order to simulate the types of loads these bones experience.
- Measurements, including strain readings from one three-gauge rosette, three distinct single-element gauges at different locations on the MC3 midshaft, associated displacement, and load exposure time, were taken during these experiments.
- Additional variables such as the age of the horse and whether the bone came from the left or right limb were also recorded.
- The researchers used this data to create input variables for their ANN model.
Findings
- The ANN model was put to the test to see how accurately it could predict the load on the MC3 bones based on the experimental data.
- The model’s predictions had a very high degree of reliability (R ≥ 0.98), showing that ANNs can effectively be used to estimate the load conditions on MC3 bones based on strain and displacement measurements.
Cite This Article
APA
Mouloodi S, Rahmanpanah H, Burvill C, Davies HMS.
(2019).
Prediction of load in a long bone using an artificial neural network prediction algorithm.
J Mech Behav Biomed Mater, 102, 103527.
https://doi.org/10.1016/j.jmbbm.2019.103527 Publication
Researcher Affiliations
- Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia; Department of Veterinary Biosciences, The University of Melbourne, Melbourne, Australia. Electronic address: saeed.mouloodi@unimelb.edu.au.
- Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia.
- Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia.
- Department of Veterinary Biosciences, The University of Melbourne, Melbourne, Australia.
MeSH Terms
- Animals
- Biomechanical Phenomena
- Finite Element Analysis
- Horses
- Metacarpal Bones
- Neural Networks, Computer
- Reproducibility of Results
Conflict of Interest Statement
Declaration of competing interest We have no conflict of interest to declare.
Citations
This article has been cited 4 times.- Naveiro JM, Gracia L, Roces J, Albareda J, Puértolas S. Three-Dimensional Computational Model Simulating the Initial Callus Growth during Fracture Healing in Long Bones: Application to Different Fracture Types.. Bioengineering (Basel) 2023 Feb 2;10(2).
- Mouloodi S, Rahmanpanah H, Burvill C, Martin C, Gohari S, Davies HMS. How Artificial Intelligence and Machine Learning Is Assisting Us to Extract Meaning from Data on Bone Mechanics?. Adv Exp Med Biol 2022;1356:195-221.
- Eerdekens A, Deruyck M, Fontaine J, Damiaans B, Martens L, De Poorter E, Govaere J, Plets D, Joseph W. Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data.. Animals (Basel) 2021 Oct 7;11(10).
- Walle M, Marques FC, Ohs N, Blauth M, Müller R, Collins CJ. Bone Mechanoregulation Allows Subject-Specific Load Estimation Based on Time-Lapsed Micro-CT and HR-pQCT in Vivo.. Front Bioeng Biotechnol 2021;9:677985.
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