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Finite element models: A road to in-silico modeling in the age of personalized dentistry
P Lahoud1, H Faghihian2, R Richert3
1OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, Leuven, Belgium; Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium; Division of Periodontology and Oral Microbiology, Department of Oral Health Sciences, KU Leuven, Leuven, Belgium.
Objective:
This article reviews the applications of Finite Element Models (FEMs) in personalized dentistry, focusing on treatment planning, material selection, and CAD-CAM processes. It also discusses the challenges and future directions of using finite element analysis (FEA) in dental care.
Data:
This study synthesizes current literature and case studies on FEMs in personalized dentistry, analyzing research articles, clinical reports, and technical papers on the application of FEA in dental biomechanics.
Sources:
Sources for this review include peer-reviewed journals, academic publications, clinical case studies, and technical papers on dental biomechanics and finite element analysis. Key databases such as PubMed, Scopus, Embase, and ArXiv were used to identify relevant studies.
Study Selection:
Studies were selected based on their relevance to the application of FEMs in personalized dentistry. Inclusion criteria were studies that discussed the use of FEA in treatment planning, material selection, and CAD-CAM processes in dentistry. Exclusion criteria included studies that did not focus on personalized dental treatments or did not utilize FEMs as a primary tool.
Conclusions:
FEMs are essential for personalized dentistry, offering a versatile platform for in-silico dental biomechanics modeling. They can help predict biomechanical behavior, optimize treatment outcomes, and minimize clinical complications. Despite needing further advancements, FEMs could help significantly enhance treatment precision and efficacy in personalized dental care.
Clinical Significance:
FEMs in personalized dentistry hold the potential to significantly improve treatment precision and efficacy, optimizing outcomes and reducing complications. Their integration underscores the need for interdisciplinary collaboration and advancements in computational techniques to enhance personalized dental care.
