Advancements in finite element analysis for prosthodontics
Journal Title: Progress in Medical Devices - Year 2024, Vol 2, Issue 4
Abstract
Finite element analysis (FEA) is a computer-aided tool widely employed in the field of prosthodontics, offering a comprehensive understanding of biomechanical behavior and assisting in the design and evaluation of dental prostheses. By dividing a model into finite elements, FEA enables accurate predictions of stress, strain, and displacement of structures. This review summarizes recent research developments in the application of FEA across various aspects of prosthodontics, including dental implant, removable partial denture, fixed partial denture and their combinations. FEA plays a significant role in selecting restoration materials, optimizing prosthetic designs, and examining the dynamic interactions between prostheses and natural teeth. Its computational efficiency and accuracy have expanded its application potentials for preoperative planning in custom-made prosthodontics. Upon the physician’s assessment of the repair requirements tailored to the individual patient’s condition, FEA can be employed to evaluate the stress distribution, displacement, and other relevant outcomes associated with the proposed restoration. When integrated with clinical expertise, it facilitates assessing design feasibility, identifying necessary adjustments, and optimizing prosthetic solutions to mitigate the risk of failure. Additionally, FEA helps identify potential complications arising from long-term prosthetics use, allowing for the implementation of preventive strategies. Presenting FEA results to patients enhances their understanding of the scientific basis and rationale behind the design, thereby bolstering patient confidence in the proposed intervention. Despite its ongoing limitations, FEA underscores the importance of integrating computational findings with clinical judgment and supplementary diagnostic tools. This review emphasizes the growing role of FEA in advancing prosthodontics by offering computational analysis and design optimization, ultimately improving treatment outcomes and patient satisfaction.
Authors and Affiliations
Yan Wang, Liwen Chen
Keywords
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- EP ID EP754637
- DOI 10.61189/974215qcjfzk
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