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Modeling the Human Mandible Under Masticatory Loads: Which Input Variables are Important?
Author(s) -
Gröning Flora,
Fagan Michael,
O'higgins Paul
Publication year - 2012
Publication title -
the anatomical record: advances in integrative anatomy and evolutionary biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.678
H-Index - 62
eISSN - 1932-8494
pISSN - 1932-8486
DOI - 10.1002/ar.22455
Subject(s) - finite element method , sensitivity (control systems) , masticatory force , mandible (arthropod mouthpart) , periodontal fiber , ligament , computer science , bite force quotient , biomechanics , orientation (vector space) , orthodontics , structural engineering , mathematics , biological system , anatomy , geometry , engineering , biology , medicine , genus , ecology , electronic engineering
Finite element analyses (FEA) that have simulated masticatory loadings of the human mandible differ significantly with regard to their basic input variables such as material properties, constraints, and applied forces. With sensitivity analyses it is possible to assess how the choice of different input values and the degree of model simplification affect FEA results. However, published FEA studies are rarely accompanied by sensitivity analyses so that the robusticity of their results is impossible to assess. Here, we conduct a sensitivity analysis with an FE model of a human mandible to quantify the relative importance of several modeling decisions: (1) the material properties assigned to the cancellous bone tissue; (2) the inclusion or not of the periodontal ligament; (3) the constraints at the joints and bite point; and (4) the orientation of applied muscle forces. We study the effects of varying these properties by analysing the strain magnitudes and directions across the model surface. In addition, we perform a geometric morphometric analysis of the deformation resulting from the loading of each model. The results show that the effects of altering the different model properties can be significant and that most effects are potentially large enough to cause problems for the biological interpretation of FEA results. We therefore recommend that researchers conduct more sensitivity analyses than at present to assess the robusticity of their FEA results and their biological conclusions. Anat Rec, 2012. © 2012 Wiley Periodicals, Inc.