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Updated: Mar 27, 2026

Author Spotlight: Development of a Novel Finite Element Analysis Model for Improved Orthognathic Surgical Techniques
Published on: October 20, 2023
The Use of Finite Element Analysis to Enhance Research and Clinical Practice in Orthopedics
1Department of Orthopaedic Surgery, University of Missouri, Columbia, Missouri.
Abstract:
Finite element analysis (FEA) is a very powerful tool for the evaluation of biomechanics in orthopedics. Finite element (FE) simulations can effectively and efficiently evaluate thousands of variables (such as implant variation, surgical techniques, and various pathologies) to optimize design, screening, prediction, and treatment in orthopedics. Additionally, FEA can be used to retrospectively evaluate and troubleshoot complications or failures to prevent similar future occurrences. Finally, FE simulations are used to evaluate implants, procedures, and techniques in a time- and cost-effective manner. In this work, an overview of the development of FE models is provided and an example application is presented to simulate knee biomechanics for a specimen with medial meniscus insufficiency. FE models require the development of the geometry of interest, determination of the material properties of the tissues simulated, and an accurate application of a numerical solver to produce an accurate solution and representation of the field variables. The objectives of this work are to introduce the reader to the application of FEA in orthopedic analysis of the knee joint. A brief description of the model development process as well as a specific application to the investigation of knee joint stability in geometries with normal or compromised medial meniscal attachment is included. Significant increases in stretch of the anterior cruciate ligament were predicted in specimens with medial meniscus insufficiency (such behavior was confirmed in corresponding biomechanical testing). It can be concluded from this work that FE analysis of the knee can provide significant new information with which more effective clinical decisions can be made.
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