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AimThe purpose of this study was to present and validate an innovative semi-automatic approach to quantify the accuracy of the surgical outcome in relation to 3D virtual orthognathic planning among patients who underwent bimaxillary surgery.Material and MethodFor the validation of this new semi-automatic approach, CBCT scans of ten patients who underwent bimaxillary surgery were acquired pre-operatively. Individualized 3D virtual operation plans were made for all patients prior to surgery. During surgery, the maxillary and mandibular segments were positioned as planned by using 3D milled interocclusal wafers. Consequently, post-operative CBCT scan were acquired. The 3D rendered pre- and postoperative virtual head models were aligned by voxel-based registration upon the anterior cranial base. To calculate the discrepancies between the 3D planning and the actual surgical outcome, the 3D planned maxillary and mandibular segments were segmented and superimposed upon the postoperative maxillary and mandibular segments. The translation matrices obtained from this registration process were translated into translational and rotational discrepancies between the 3D planning and the surgical outcome, by using the newly developed tool, the OrthoGnathicAnalyser. To evaluate the reproducibility of this method, the process was performed by two independent observers multiple times.ResultsLow intra-observer and inter-observer variations in measurement error (mean error < 0.25 mm) and high intraclass correlation coefficients (> 0.97) were found, supportive of the observer independent character of the OrthoGnathicAnalyser. The pitch of the maxilla and mandible showed the highest discrepancy between the 3D planning and the postoperative results, 2.72° and 2.75° respectively.ConclusionThis novel method provides a reproducible tool for the evaluation of bimaxillary surgery, making it possible to compare larger patient groups in an objective and time-efficient manner in order to optimize the current workflow in orthognathic surgery.

The present findings support the hypothesis that children with ADHD present sleep disturbances.

The implementation of augmented reality (AR) in image-guided surgery (IGS) can improve surgical interventions by presenting the image data directly on the patient at the correct position and in the actual orientation. This approach can resolve the switching focus problem, which occurs in conventional IGS systems when the surgeon has to look away from the operation field to consult the image data on a 2-dimensional screen. The Microsoft HoloLens, a headmounted AR display, was combined with an optical navigation system to create an AR-based IGS system. Experiments were performed on a phantom model to determine the accuracy of the complete system and to evaluate the effect of adding AR. The results demonstrated a mean Euclidean distance of 2.3 mm with a maximum error of 3.5 mm for the complete system. Adding AR visualization to a conventional system increased the mean error by 1.6 mm. The introduction of AR in IGS was promising. The presented system provided a solution for the switching focus problem and created a more intuitive guidance system. With a further reduction in the error and more research to optimize the visualization, many surgical applications could benefit from the advantages of AR guidance.

The approximity of the inferior alveolar nerve (IAN) to the roots of lower third molars (M3) is a risk factor for the occurrence of nerve damage and subsequent sensory disturbances of the lower lip and chin following the removal of third molars. To assess this risk, the identification of M3 and IAN on dental panoramic radiographs (OPG) is mandatory. In this study, we developed and validated an automated approach, based on deep-learning, to detect and segment the M3 and IAN on OPGs. As a reference, M3s and IAN were segmented manually on 81 OPGs. A deep-learning approach based on U-net was applied on the reference data to train the convolutional neural network (CNN) in the detection and segmentation of the M3 and IAN. Subsequently, the trained U-net was applied onto the original OPGs to detect and segment both structures. Dice-coefficients were calculated to quantify the degree of similarity between the manually and automatically segmented M3s and IAN. The mean dice-coefficients for M3s and IAN were 0.947 ± 0.033 and 0.847 ± 0.099, respectively. Deep-learning is an encouraging approach to segment anatomical structures and later on in clinical decision making, though further enhancement of the algorithm is advised to improve the accuracy.

Summary Background Miniscrew-Assisted Rapid Palatal Expansion (MARPE) is a non-surgical treatment for transverse maxillary deficiency. However, there is limited evidence concerning its efficacy. Objectives This systematic review aims to evaluate the efficacy of MARPE in late adolescents and adults by assessing success rate and skeletal and dental transverse maxillary expansion, as well as treatment duration, dental and periodontal side effects and soft tissue effects. Search methods Seven electronic databases were searched (MEDLINE, Embase, Cochrane Library, Web of Science, Scopus, ProQuest and ClinicalTrials.gov) without limitations in November 2020. Selection criteria Randomized and non-randomized clinical trials and observational studies on patients from the age of 16 onwards with transverse maxillary deficiency who were treated with MARPE and which included any of the predefined outcomes. Data collection and analysis Inclusion eligibility screening, data extraction and risk of bias assessment were performed independently in duplicate. When possible, exploratory meta-analyses of mean differences (MDs) with their 95% confidence intervals (CIs) were conducted, followed by the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) analysis of the evidence quality. Results Eight articles were included: two prospective and six retrospective observational studies. One study had a moderate risk of bias, whereas seven studies had a serious risk of bias. GRADE quality of evidence was very low. MARPE showed a high success rate (mean: 92.5%; 95%CI: 88.7%–96.3%), resulting in a significant skeletal width increase (MD: 2.33 mm; 95%CI: 1.63 mm–3.03 mm) and dental intermolar width increase (MD: 6.55 mm; 95%CI: 5.50 mm–7.59 mm). A significant increase in dental tipping, a decrease in mean buccal bone thickness and buccal alveolar height, as well as nasal soft tissue change was present (P &lt; 0.05). The mean duration of expansion ranged from 20 to 126 days. Limitations One of the main drawbacks was the lack of high-quality prospective studies in the literature. Conclusions and implications MARPE is a treatment modality that is associated with a high success rate in skeletal and dental maxillary expansion. MARPE can induce dental and periodontal side effects and affect peri-oral soft tissues. Given the serious risk of bias of the included studies, careful data interpretation is necessary and future research of higher quality is strongly recommended. Registration PROSPERO (CRD42020176618). Funding No grants or any other support funding were received.

ObjectivesThe purpose of this study was to assess the feasibility of 3D intraoral scanning for documentation of palatal soft tissue by evaluating the accuracy of shape, color, and curvature.Materials and methodsIntraoral scans of ten participants’ upper dentition and palate were acquired with the TRIOS® 3D intraoral scanner by two observers. Conventional impressions were taken and digitized as a gold standard. The resulting surface models were aligned using an Iterative Closest Point approach. The absolute distance measurements between the intraoral models and the digitized impression were used to quantify the trueness and precision of intraoral scanning. The mean color of the palatal soft tissue was extracted in HSV (hue, saturation, value) format to establish the color precision. Finally, the mean curvature of the surface models was calculated and used for surface irregularity.ResultsMean average distance error between the conventional impression models and the intraoral models was 0.02 ± 0.07 mm (p = 0.30). Mean interobserver color difference was − 0.08 ± 1.49° (p = 0.864), 0.28 ± 0.78% (p = 0.286), and 0.30 ± 1.14% (p = 0.426) for respectively hue, saturation, and value. The interobserver differences for overall and maximum surface irregularity were 0.01 ± 0.03 and 0.00 ± 0.05 mm.ConclusionsThis study supports the hypothesis that the intraoral scan can perform a 3D documentation of palatal soft tissue in terms of shape, color, and curvature.Clinical relevanceAn intraoral scanner can be an objective tool, adjunctive to the clinical examination of the palatal tissue.

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