Research on whether the number or location of missing teeth affects the accuracy of intraoral scanners in partial edentulous patients is scarce. This study aimed to evaluate the precision of complete-arch scan data of various partial edentulous arches acquired by intraoral scanners. Five different maxillary models were scanned using Carestream CS3600 and Medit i500 scanners. The models employed here were control: Fully dentate; Case 1: Missing a right second premolar and a first molar; Case 2: Missing a right second premolar, a first molar, both left premolars, and a left first molar; Case 3: Missing four incisors and a right canine; and Case 4: Missing four incisors, a left second premolar, and a first molar. Six scans per group were performed and the resulting two datasets were paired to analyze the precision of each group (n = 15). Two-way ANOVA was performed (α = 0.05). The root mean square (RMS) error values in Cases 2, 3, and 4 were significantly higher than those in Case 1 and control. The RMS values of the two intraoral scanners were not significantly different. Scanning precision was significantly lower for both devices when used for scanning dental arches with ≥5 missing teeth.

Without expert coaching, inexperienced exercisers performing core exercises, such as squats, are subject to an increased risk of spinal or knee injuries. Although it is theoretically possible to measure the kinematics of body segments and classify exercise forms with wearable sensors and algorithms, the current implementations are not sufficiently accurate. In this study, the squat posture classification performance of deep learning was compared to that of conventional machine learning. Additionally, the location for the optimal placement of sensors was determined. Accelerometer and gyroscope data were collected from 39 healthy participants using five inertial measurement units (IMUs) attached to the left thigh, right thigh, left calf, right calf, and lumbar region. Each participant performed six repetitions of an acceptable squat and five incorrect forms of squats that are typically observed in inexperienced exercisers. The accuracies of squat posture classification obtained using conventional machine learning and deep learning were compared. Each result was obtained using one IMU or a combination of two or five IMUs. When employing five IMUs, the accuracy of squat posture classification using conventional machine learning was 75.4%, whereas the accuracy using deep learning was 91.7%. When employing two IMUs, the highest accuracy (88.7%) was obtained using deep learning for a combination of IMUs on the right thigh and right calf. The single IMU yielded the best results on the right thigh, with an accuracy of 58.7% for conventional machine learning and 80.9% for deep learning. Overall, the results obtained using deep learning were superior to those obtained using conventional machine learning for both single and multiple IMUs. With regard to the convenience of use in self-fitness, the most feasible strategy was to utilize a single IMU on the right thigh.

High‐translucency restorative materials are commonly used in the restoration of anterior teeth where aesthetics is a critical factor. In this in vitro study, the impact of mouthwash on the colour stability and surface characteristics of high‐translucency computer‐aided design and computer‐aided manufacturing (CAD‐CAM) dental restorative materials was evaluated. Two‐hundred specimens were fabricated from five high‐translucency CAD‐CAM materials: a resin nano ceramic; a polymer‐infiltrated ceramic network; a feldspathic ceramic; a lithium disilicate glass ceramic; and high‐translucency zirconia. Each group of ceramic specimens was then divided into four subgroups: conventional mouthwash (LISTERINE); whitening mouthwash (LISTERINE Healthy White); chlorhexidine gluconate; and distilled water. Oral rinsing was simulated at 100 rpm for 180 h, representing 15 yr of clinical simulation. The specimens were then evaluated for colour, translucency, gloss, roughness, and surface morphology. Two‐way ANOVA and linear mixed models were used for intergroup comparisons (α = 0.05). The polymer‐infiltrated ceramic network and feldspathic ceramic became brighter, more opaque, less glossy, and rougher after rinsing with the whitening mouthwash. The long‐term use of specific mouthwashes can cause deterioration of the optical and surface properties of high‐translucency CAD‐CAM dental restorations.

This in vitro study investigated the impact of various dentifrices on the shade, translucency, gloss, and surface characteristics of polishing- or glazing-finished monolithic zirconia surfaces after simulated toothbrushing. Eighty square-shaped monolithic zirconia specimens were divided into two major groups based on the finishing methods—polished (P) and glazed (G). Next, specimens from the two major groups were categorized into four subgroups: stored in distilled water (DW, control); brushed with a fluoride-free conventional dentifrice (C); brushed with a fluoride dentifrice (F); and brushed with a whitening dentifrice (W). Overall, eight groups were created—PDW, PC, PF, PW, GDW, GC, GF, and GW (n = 10 each). Shade, translucency, surface gloss, surface roughness, crystalline phase, and superficial topography data were obtained. Repeated-measures ANOVA and two-way ANOVA were used for intergroup comparison (all α = 0.05). The color differences (ΔE00) between pre- and posttreatment were 0.3158 (PDW), 0.7164 (PC), 0.7498 (PF), 0.8106 (PW), 0.1953 (GDW), 0.301 (GC), 0.3051 (GF), and 0.4846 (GW). A statistically significant difference was observed among the ΔE00, surface gloss, and surface roughness of monolithic zirconia. Thus, brushing with several dentifrices markedly affects the optical properties and surface characteristics of monolithic zirconia finished with polishing or glazing methods.

PURPOSE The purpose of this in vitro study was to investigate the wear resistance and surface roughness of three interim resin materials, which were subjected to chewing simulation. MATERIALS AND METHODS Three interim resin materials were evaluated: (1) three-dimensional (3D) printed (digital light processing type), (2) computer-aided design and computer-aided manufacturing (CAD/CAM) milled, and (3) conventional polymethyl methacrylate interim resin materials. A total of 48 substrate specimens were prepared. The specimens were divided into two subgroups and subjected to 30,000 or 60,000 cycles of chewing simulation (n = 8). The wear volume loss and surface roughness of the materials were compared. Statistical analysis was performed using one-way analysis of variance and Tukey's post-hoc test (α=.05). RESULTS The mean ± standard deviation values of wear volume loss (in mm 3 ) against the metal abrader after 60,000 cycles were 0.10 ± 0.01 for the 3D printed resin, 0.21 ± 0.02 for the milled resin, and 0.44 ± 0.01 for the conventional resin. Statistically significant differences among volume losses were found in the order of 3D printed, milled, and conventional interim materials ( P <.001). After 60,000 cycles of simulated chewing, the mean surface roughness (Ra; μm) values for 3D printed, milled, and conventional materials were 0.59 ± 0.06, 1.27 ± 0.49, and 1.64 ± 0.44, respectively. A significant difference was found in the Ra value between 3D printed and conventional materials ( P =.01). CONCLUSION The interim restorative materials for additive and subtractive manufacturing digital technologies exhibited less wear volume loss than the conventional interim resin. The 3D printed interim restorative material showed a smoother surface than the conventional interim material after simulated chewing.

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Rationale: Full-mouth rehabilitation of patients with bruxism and severely worn dentition poses a great challenge to clinicians. Several treatment planning methods and restorative materials are used to treat tooth wear in modern dentistry. Clinicians should be able to select the most suitable treatment planning methods and materials for individual patients depending on their specific situation. Patient concerns: A 47-year-old male was referred for evaluation of a severely worn dentition. Diagnoses: Clinical and radiographic evaluation revealed tooth wear in the entire dentition. The interocclusal distance at rest was 4 mm, and the patient had a parafunctional habit of bruxism. Interventions: A digital smile design was used to formulate a treatment plan. Full-mouth rehabilitation was performed using a combination of conventional and digital materials and methods. Outcomes: The full-mouth restoration showed satisfactory functions and esthetics. No complications were observed in the restorations, supporting tissues, and temporomandibular joints during 2-year follow-up. Lessons: In clinical practice, it is important to determine the optimal combination of the available methods for treatment planning. This case report details the formulation of a unique treatment plan for the dental rehabilitation of a severely worn out dentition, which is considered challenging due to the limitations imposed by biological tissues and restorative materials. The use of conventional and digital tools for treatment planning, patient education, and treatment execution was demonstrated.

Fifteen participants (9 male, 6 female) received a total of 15 monolithic single restorations made from fully sintered (Y, Nb)-TZP (tetragonal zirconia polycrystal) block. The restorations were clinically evaluated for survival, success rate, and periodontal probing depths 6 months after the insertion of the restorations. Esthetic, functional, and biological evaluations were also performed over a 6-month follow-up period. The survival and success rates of the single-unit restorations were 100%. The periodontal probing depth values ranged from 1 to 3 mm. No complications with regard to functional and biological properties were observed after 6 months. The postoperative sensitivity was only a transient phenomenon. The fully sintered (Y, Nb)-TZP single-unit restoration showed highly acceptable quality with successful clinical performance over 6 months.