Polyetheretherketone (PEEK) is a new material used for the frameworks of removable partial dentures (RPD). The questions whether the PEEK framework has similar stress distribution on oral tissue and displacement under masticatory forces as titanium alloy (Ti-6Al-4V) or cobalt-chromium alloy (CoCr) remain unclear and worth exploring. A patient’s intraoral data were obtained via CBCT and master model scan. Four RPDs were designed by 3Shape dental system, and the models were processed by three-dimensional finite element analysis. Among three materials tested, PEEK has the lowest maximum von Mises stress (VMS) on periodontal ligament (PDL), the greatest maximum VMS on mucosa, the maximum displacement on free-end of framework, and the lowest maximum VMS on framework. Results suggested that PEEK framework has a good protective effect on PDL, suggesting applications for patients with poor periodontal conditions. However, the maximum displacement of the free-end under masticatory force is not conducive for denture stability, along with large stress on the mucosa indicate that PEEK is unsuitable for patients with more loss of posterior teeth with free-end edentulism.

Making impressions in patients with microstomia is often rather problematic due to their restricted mouth opening. Herein, this report describes a novel digital workflow for making impressions with computer‐aided design and computer‐aided manufacturing (CAD/CAM) custom sectional trays for a 58‐year‐old female patient with scleroderma and microstomia. CAD/CAM custom sectional trays were made based on digital dentition models from another case with similar arch scale. After the sectional impressions were obtained, the sectional casts were scanned and digitally aligned to form the final dentition models. The removable partial dentures were designed on the final digital models and printed using a 3D printer. This procedure was executed with a successful prosthetic outcome that included good fit and acceptable esthetics. The patient also reported a high level of satisfaction.

Introduction Long-term simulation of tooth movement is crucial for clear aligner (CA) treatment. This study aimed to investigate the effect of maxillary molar distalization with CA via an automatic staging simulation. Method A finite-element method (FEM) model of maxillary dentition, periodontal ligaments, attachments, and corresponding CA was established, and a prescribed 2-mm distalization with 0.1 mm each step of the second molar was simulated. The long-term tooth movement under orthodontic force was simulated with an iterative computation method. The morphologic changes of CA during staging were simulated with the thermal expansion method. Results Twenty steps of molar distalization were simulated. Significant distal tilting of the second molar was revealed, along with the proclination of anterior teeth, which caused the ‘reversed bow effect’. For the second molar, 4.63°distal tilting at the 20th step was revealed. The intrusion of the incisors and the second molar were 0.43 mm, 0.39 mm, and 0.45 mm, respectively, at step 20. All the anterior teeth showed a proclination of approximately 1.41°–2.01° at the 20th step. The expression rate of the designed distalization of the second molar was relatively low (approximately 68%) compared to the high efficacy of interdental space opening between molars with CA (approximately 89%). Conclusion A novel method of simulating long-term molar distalization with CA with FEM was developed. The FEM results suggested distal tilting of the second molar and the proclination of anterior teeth during the molar distalization.

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Background This study aims to investigate the accuracy of a three-dimensional (3D) face reconstruction method based on conventional clinical two-dimensional (2D) photos. Methods Twenty-three patients were included, and Character Creator v3.2 software with the Headshot v1.0 plugin was used for 3D face model reconstruction. Various facial landmarks were finely adjusted manually to refine the models. After preprocessing and repositioning, 3D deviation analysis was performed. The accuracy of the landmarks in different dimensions was determined, and twelve facial soft tissue measurements were compared to validate the clinical potential of the method. Result The reconstructed 3D face models showed good facial morphology with fine texture. The average root mean square errors between face scan models and reconstructed models at perioral area (1.26 ± 0.24 mm, 95%CI: 1.15–1.37 mm) were significantly smaller than the entire facial area (1.77 ± 0.23 mm, 95%CI:1.67–1.88 mm), P < 0.01. The deviation of menton of soft tissue was significantly larger than pronasale (P < 0.01). The deviations of all landmarks in the Y-direction were significantly larger than those in the other 2 dimensions (Y > Z > X, P < 0.01). A significant difference (P < 0.05) of approximately 1.5 mm was found for facial height. Significant differences (P < 0.05) were also identified in the remaining 6 soft tissue measurements, with average deviations no greater than 0.5 mm (linear measurement) or 1.2° (angular measurements). Conclusion A 3D face modeling method based on 2D face photos was revealed and validated. The reconstruction accuracy of this method is clinically acceptable for orthodontic measurement purposes, but narrow clinical indications and labor-intensive operations remain problems.

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Objective: To explore the influence of modified literature classification strategies of Chinese biomedical literature on an automated screener based on conventional algorithm.Methods: Citations of studies indexed as ‘Oral Science’ published in Chinese between 2014 and 2018 were retrieved from the China National Knowledge Infrastructure. Apart from dividing the studies into 2 categories (RCTs and non-RCTs), 3-category (RCTs, may-be-RCTs, and non-RCTs) and 5-category (RCTs, randomization-unclear controlled trials, non-randomized clinical trials/studies, non-clinical literature, and unclear) classification were also employed. The multi-category strategies took into consideration the diversity of study types and the presence of expression vagueness. Similar to real-world practice, full-text-needed studies included those that certainly concerned RCTs and those that might be RCTs but lacked information in their abstracts. Screening and classification were performed independently by 2 experienced researchers. The classification results after peer discussion and/or senior decision were used for the training of the CNN model. The probability thresholds for the classification of each category were set at a high sensitivity level.The area under the receiver-operator curve (AUC) was calculated when applicable. An isolated sample of citations was used in a prospective comparative trial that compared the sensitivity (SEN) and specificity (SPE) of screening RCTs, may-be-RCTs, and full-text-needed studies by using algorithms with different strategies and manual screening.Results:In total, 12,166 citations were used for CNN model training. All 3 training strategies performed well in RCTs-screening with AUCs being higher than 0.99. The training exhibited that, when screening for RCTs, the 5- and 3-category strategies can yield better performance than the 2-category strategy. When screening for may-be-RCTs and full text-needed studies, the 5-category model achieved better SENs while the 3-category model achieved higher SPEs. The comparative trial with 1,422 samples presented similar results.Conclusion: The CNN algorithm has promising results in the automatic screening of Chinese literature. The multi-category training strategies considering different study types and expression vagueness are more suitable for CNN training and can help achieve better screening sensitivity and specificity.

Objectives To determine the expansion rebound deformation (ERD) of clear aligners (CAs) and its biomechanical influence. Materials and Methods A four-premolar extraction treatment plan was carried out for a patient with 2 CA companies. Thirty-six digitally scanned clear aligners with the corresponding 36 virtually constructed “ideal” aligners were constructed. The arch width and length between pairs of reference landmarks of the scanned CAs and corresponding dentition models were measured. Cone-beam computed tomography data and digital dental models were used for three-dimensional (3D) finite element analysis (FEA) modeling. Thirty-six scanned CA models with the corresponding 36 ideal CA models were constructed. One-way analysis of variance was used to determine the differences among deviation values at tooth level, and paired t-test was used to compare the displacements of teeth between the two group of CAs. Results All CAs were wider and shorter than the digital model from which they were constructed. In the scanned CA model group, significant stress was observed in the buccolingual area of the periodontal ligament on posterior teeth, and the corresponding displacements of teeth were also noted. Significantly larger coronal displacements were noted for the lateral incisor, the canine, the second premolar, and the first molar in the scanned CA group (P &lt; .05). Conclusions The general trend of ERD of thermoformed CAs was shown. This deformation may cause unforeseen tooth movements and negatively affect treatment outcomes.