Search

This systematic review was performed to assess the potential influence of orthognathic surgery on root resorption (RR).An electronic search was conducted using PubMed, Web of Science, Cochrane Central and Embase for articles published up to April 2022. Following inclusion and exclusion criteria, a total of six articles were selected that reported on RR following orthognathic surgery. Risk of bias assessment was performed according to the ROBINS-1 and ROB-2 tools.The design of five studies was retrospective and one randomized clinical trial was included, with a follow-up period ranging between six months and ten years. The assessment methodologies mostly relied on two-dimensional imaging modalities where only one study used cone-beam computed tomography (CBCT) for objective quantification via linear measurements. The percentage of teeth affected by RR varied between approximately 1 and 36%, where surgically assisted rapid maxillary expansion (SARME) and Le Fort I osteotomy showed the highest percentage of RR followed by bilateral sagittal split osteotomy.The present data tend to indicate that specific orthognathic procedures such as SARME and Le Fort I osteotomy may induce or reinforce RR. Yet, considering lack of evidence related to objective quantification of RR following orthodontic and/or orthognathic treatment, further CBCT-based prospective studies are required for an improved understanding of RR following different surgical procedures.

Objective To qualitatively and quantitatively assess integrated segmentation of three convolutional neural network (CNN) models for the creation of a maxillary virtual patient (MVP) from cone-beam computed tomography (CBCT) images. Materials and methods A dataset of 40 CBCT scans acquired with different scanning parameters was selected. Three previously validated individual CNN models were integrated to achieve a combined segmentation of maxillary complex, maxillary sinuses, and upper dentition. Two experts performed a qualitative assessment, scoring-integrated segmentations from 0 to 10 based on the number of required refinements. Furthermore, experts executed refinements, allowing performance comparison between integrated automated segmentation (AS) and refined segmentation (RS) models. Inter-observer consistency of the refinements and the time needed to create a full-resolution automatic segmentation were calculated. Results From the dataset, 85% scored 7–10, and 15% were within 3–6. The average time required for automated segmentation was 1.7 min. Performance metrics indicated an excellent overlap between automatic and refined segmentation with a dice similarity coefficient (DSC) of 99.3%. High inter-observer consistency of refinements was observed, with a 95% Hausdorff distance (HD) of 0.045 mm. Conclusion The integrated CNN models proved to be fast, accurate, and consistent along with a strong interobserver consistency in creating the MVP. Clinical relevance The automated segmentation of these structures simultaneously could act as a valuable tool in clinical orthodontics, implant rehabilitation, and any oral or maxillofacial surgical procedures, where visualization of MVP and its relationship with surrounding structures is a necessity for reaching an accurate diagnosis and patient-specific treatment planning.

The aim of this study was to quantify the symmetry of the facial hard tissue structures using three-dimensional radiographic imaging modalities in a normal Caucasian population group. Electronic literature search was conducted in the following databases: PubMed, Embase, Web of Science, and Cochrane Library up to February 2021. The studies assessing symmetry of facial bones using computed tomography (CT) and cone beam CT were included. The initial search revealed 8811 studies. Full-text analysis was performed on 33 studies. Only 10 studies were found eligible based on the inclusion criteria. The qualitative analysis revealed that a significant variability existed in relation to the methodologies applied for symmetry quantification. The current review suggested that the overall relative symmetry of the normal Caucasian population group varied depending on the skeletal structure being assessed; however, majority of the observations showed a symmetry within the range of 1 mm without any significant difference between left and right sides. The quantification of facial hard tissue structure symmetry is vital for the diagnosis and treatment planning of orthodontic and/or maxillofacial surgical procedures. Prospero registration number CRD42020169908

The present study investigated the accuracy, consistency, and time-efficiency of a novel deep convolutional neural network (CNN) based model for the automated maxillofacial bone segmentation from cone beam computed tomography (CBCT) images.A dataset of 144 scans was acquired from two CBCT devices and randomly divided into three subsets: training set (n = 110), validation set (n = 10) and testing set (n = 24). A three-dimensional (3D) U-Net (CNN) model was developed, and the achieved automated segmentation was compared with a manual approach.The average time required for automated segmentation was 39.1 s with a 204-fold decrease in time consumption compared to manual segmentation (132.7 min). The model was highly accurate for identification of the bony structures of the anatomical region of interest with a dice similarity coefficient (DSC) of 92.6%. Additionally, the fully deterministic nature of the CNN model was able to provide 100% consistency without any variability. The inter-observer consistency for expert-based minor correction of the automated segmentation observed an excellent DSC of 99.7%.The proposed CNN model provided a time-efficient, accurate, and consistent CBCT-based automated segmentation of the maxillofacial complex.Automated segmentation of the maxillofacial complex could act as an alternative to the conventional segmentation techniques for improving the efficiency of the digital workflows. This approach could deliver accurate and ready-to-print3D models, essential to patient-specific digital treatment planning for orthodontics, maxillofacial surgery, and implant dentistry.

An accurate three-dimensional (3D) segmentation of the maxillary sinus is crucial for multiple diagnostic and treatment applications. Yet, it is challenging and time-consuming when manually performed on a cone-beam computed tomography (CBCT) dataset. Recently, convolutional neural networks (CNNs) have proven to provide excellent performance in the field of 3D image analysis. Hence, this study developed and validated a novel automated CNN-based methodology for the segmentation of maxillary sinus using CBCT images. A dataset of 264 sinuses were acquired from 2 CBCT devices and randomly divided into 3 subsets: training, validation, and testing. A 3D U-Net architecture CNN model was developed and compared to semi-automatic segmentation in terms of time, accuracy, and consistency. The average time was significantly reduced (p-value

Aim of the study: The current study was conducted to determine the accuracy of QuickScan imaging protocols of iCAT Flx CBCT machine in detection and measuring crestal bone defects around dental implants.Materials and Methods: 20 simulated peri-implant crestal bone defects were created in bovine ribs bone models then were scanned by iCAT FLX CBCT machine with HD Scan 0.25 mm voxel, QuickScan 0.25mm voxel, and QuickScan + 0.3mm voxel sizes. All scans were imported in on-demand 3D App software and produced linear measurements were compared to true measurements done directly with digital caliper on bone models.Results: All imaging protocols of CBCT showed no statistical significance in detection of shallow peri-implant bone defects while the QuickScan + imaging protocol showed less accuracy in measuring the depth of defects.Conclusion: QuickScan + can be used as a low dose periodic follow up imaging utility for postoperative implant surgery for detection of any small bone changes but if more details are needed QuickScan technique is required.