Computational Investigation of Dental Implant Restoration Using Platform-Switched and -Matched Configurations

Abstract

Dental trauma is a serious and highly prevalent health issue across the globe. Most of the frequent dental injuries result in the loss of teeth and affects the overall quality of life. The loss of a tooth is usually compensated by a dental implant. The common methods adopted while placing the implant tooth are platform switching and platform matching. A plethora of works has studied the qualitative performance of these methods across different situations clinically. However, a detailed comparative work studying in-depth the mechanical parameters has not been attempted yet. In this computational work, two commonly available different platform-switched and one platform-matched implant-abutment configurations were compared. A 3D model of an implant (5.5 × 9.5 mm) was designed and inserted into a human mandibular bone block using computer-aided design (CAD) and extracting the clinical imaging data. Three separate models of implant-abutment configurations such as Platform Switched (PS)-I, a 5.5 mm implant with a 3.8 mm wide abutment, Platform Switched (PS)-II, a 5.5 mm implant with a 4.5 mm wide abutment, and Platform Matched (PM), a 5.5-mm implant with a 5.5 mm wide abutment were analyzed. Clinically relevant vertical-, horizontal-, and oblique-type of occlusal loadings were applied to each model to characterize the mechanical response. Mechanical parameters such as von Mises stresses, deformations, and strain energies were obtained using finite element modeling (FEM). These parameters showed lower values for platform switching within the peri-implant bone and that may help to limit marginal bone loss. However, the same parameters were increasing more in the abutment, implant, and screw for the platform-switched implant configuration than that of platform-matched configuration. The computational framework, along with the results, are anticipated to guide the clinicians and medical practitioners in making better decisions while selecting the commonly available methods.