Machine (Deep) learning and finite element modeling

Yan Ting Lee, Tai Hsien Wu, Mei Ling Lin, Ching Chang Ko

Research output: Chapter in Book/Report/Conference proceedingChapter


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.

Original languageEnglish (US)
Title of host publicationMachine Learning in Dentistry
PublisherSpringer International Publishing
Number of pages6
ISBN (Electronic)9783030718817
ISBN (Print)9783030718800
StatePublished - Jul 24 2021


  • FEA
  • Machine learning

ASJC Scopus subject areas

  • General Dentistry
  • General Computer Science


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