Abstract
Finite element analysis (FEA) has been widely used to predict the biomechanical performance of various dental applications such as orthodontic tooth movement, implant components, and peri-implant bone. We begin with a brief introduction of the traditional FEA process and disadvantages of using FEA in clinical applications. Then, we review existing studies in which researchers use machine learning (ML) to address these disadvantages. Finally, we conclude that the combination of the FEA and ML is the best solution given that ML can facilitate the FEA computation, and FEA results can also enhance the accuracy of ML prediction.
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Lee, YT., Wu, TH., Lin, ML., Ko, CC. (2021). Machine (Deep) Learning and Finite Element Modeling. In: Ko, CC., Shen, D., Wang, L. (eds) Machine Learning in Dentistry. Springer, Cham. https://doi.org/10.1007/978-3-030-71881-7_14
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DOI: https://doi.org/10.1007/978-3-030-71881-7_14
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