Reliability of wear measurements of CAD-CAM restorative materials after artificial aging in a mastication simulator
Source abstract
Abstract
Objectives The purpose of this in vitro study was to assess the 2-body wear behavior of computer-aided design and computer-aided manufacturing (CAD-CAM) restorative materials and additionally to validate an automatic approach to wear quantification as provided by newly developed software. Materials and methods Two composite resins (Experimental Composite, Shofu Block HC), 1 polymethyl methacrylate- (PMMA)-based material (Telio CAD), and 1 dual-network material (VITA Enamic) were tested (n = 12). The materials were stored in distilled water at 37 °C for 14 days, then aged in a mastication simulator (cycles: 400 000, 50 N, 1.2 Hz, 5/55 °C) for 2-body wear testing with human molars as antagonists. Software was developed as a freely available function in R for automatic wear quantification and was applied to 2 (vertical and horizontal) images obtained by an optical laser scanner (SDM). Wear measurements were validated with a reference confocal laser scanning microscope (LSM) system. The Kruskal-Wallis with Tukey-Kramer post hoc test (α = 0.05), coefficient of variation, Bland-Altman method, and intraclass correlation were applied. Results Shofu Block HC showed the greatest wear, and no differences were found among other materials. The automatic wear measurements agreed well with the reference LSM system. The reliability of the SDM scans was 98.5%. Conclusions The wear resistance caused by two-body wear differed among the restorative materials tested, with the mean wear resistance of the 2 composite resin materials differing significantly. Wear quantification with R software based on SDM scans showed satisfactory agreement with the reference LSM system.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Dental materials and restorations · Dental Health and Care Utilization · Dental Erosion and Treatment
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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