Dynamic Finite Element Analysis of Quantitative Percussion Diagnostics for Microgap Defects Detection in Teeth
Abstract
Microgap defects (MGDs), including cracks and fractures in teeth, can lead to irreversible damage, potentially leading to tooth extraction. Early detection of MGDs is pivotal in avoiding permanent defects through appropriate treatments. Quantitative percussion diagnostics (QPD) is a non-destructive method that has been shown to detect MGDs in teeth. The goal of is the present research was to use finite element analysis (FEA) models to better understand the behavior of various MGDs in different tooth geometries and the patterns of QPD force responses generated for teeth with MGDs. This present research analyzed the shape of force versus time plots of QPD results and how they are affected by different MGD types and conditions in both single rooted premolars and double rooted molars. FEA models were constructed based on scanned, converted, and meshed 3D tooth geometries, and by implementing various MGD types, such as vertical cracks, oblique cracks, apical root cracks, damaged PDL and PDL lesions. FEA models were also exercised with different MGD conditions, including crack lengths and gap spaces. A percussion rod contains a force sensor, which was also included in all FEA models to simulate the clinical QPD measurements, capturing percussion force as a function of time. The simulated QPD measurements are analyzed, validated, and discussed in the present work. The results of FEA models are consistent with observed in vivo clinical results and confirm the capability of QPD for detecting MGDs in teeth. These FEA results not only provide insights into the distinctive characteristics of results corresponding to various MGD types and conditions but also explain the reasons contributing to shape changes in percussion force versus time results. Furthermore, a novel approach involving 3D printed replica teeth was also developed to address the limitation of obtaining in vivo conformational data. This innovative method enhances the foundation of the study by using replica teeth to support the understanding gained from FEA simulations.