Purpose: This study aimed to determine the usefulness of machine learning techniques, specifically supervised and unsupervised learning, for assessing the cementation condition between a fixed partial denture (FPD) and its abutment using a resonance frequency analysis (RFA) system.
Methods: An in vitro mandibular model was used with a single crown and three-unit bridge made of a high-gold alloy. Two cementation conditions for the single crown and its abutment were set: cemented and uncemented.
Background: Orofacial pain conditions are complex disorders that involve biological, social, and psychological factors. Temporomandibular Disorders (TMDs) are one of the most common orofacial pain conditions, and our previous literature review indicated that exercise therapy has shown promise in reducing TMD-related pain. However, more evidence is needed to firmly establish its effectiveness.
View Article and Find Full Text PDFAims: The purpose of this in-vitro study was to evaluate the screw loosening of two different forms of implant abutment connection designs, and two implant diameters by measuring removal torque value (RTV) before and after cyclic loading.
Materials And Methods: Twenty implant fixtures were divided equally into 2 groups (N=10): group I fixture with conical hybrid connection (CH), and group II fixture with internal hex connection (IH). Each group was divided equally into two subgroups according to implant diameters: subgroup A (3.