Objective assessment of the combined effect of exomass-related- and motion artefacts in cone beam CT

Objectives:To assess quantitatively the combined effect of exomass-related- and motion artefacts on voxel value parameters in cone beam CT (CBCT).Methods:A cylindrical phantom was manufactured, containing 21 tubes filled with a radiopaque solution, allowing the inclusion of three titanium implants in the periphery to induce exomass-related artefacts. The phantom was mounted on a robot simulating 0.75-, 1.5-, and 3 mm movements (nodding/lateral rotation/tremor). CBCT images with/without exomass and with/without movements were acquired in duplicate in three units: Cranex 3Dx, Orthophos SL-3D, and X1 (with motion-artefact correction). A cylindrical volume of interest was defined in each tube and voxel value mean and standard deviation were assessed. For each CBCT volume, the 21 mean voxel values were averaged providing the overall mean voxel value (MVV), and the standard deviation (among the 21 values) was calculated providing overall voxel value inhomogeneity (VVI). The standard deviation from each of the 21 volumes-of-interest were averaged, providing overall image noise (IN). The effect of the diverse tested situations was inferred from a repeated-measures analysis of variance, followed by Sidak’s test (α = 0.05).Results:Overall, images acquired with exomass showed significantly (p ≤ 0.05) lower MVV, and higher VVI and IN. Motion artefacts aggravated exomass-related alterations. MVV and VVI were mostly affected by 3 mm nodding movements. Motion-artefact correction eliminated the deleterious effect of movement.Conclusions:CBCT voxel-value parameters are altered by exomass-related artefacts, and this finding is aggravated in the presence of motion artefacts. Motion-artefact correction effectively eliminated the deleterious impact of movement.     

Development of a model of soft tissue simulation using ballistic gelatin for CBCT acquisitions related to dentomaxillofacial radiology research

Objectives:To present the ballistic gelatin as a new material capable of simulating the soft tissues in cone-beam CT (CBCT) images.Methods:CBCT images of three piglet heads were acquired with their soft tissues intact (standard group). Subsequently, the piglet heads were fixed in a container using metallic pins and moulded with acrylic resin; the soft tissues were then removed and replaced by ballistic gelatin, with the same thickness of the original soft tissues. The images were evaluated by two oral radiologists, to check the adaptation on bone surfaces, thickness and density, penetration into large bone cavities and cancellous bone, and the presence of air bubbles using a 5-score scale. Additionally, an objective analysis was carried out by one oral radiologist. For each CBCT scan, three axial reconstructions were selected to represent the mandibular, occlusal, and maxillary levels. The mean and standard deviation (SD) of the grey values were calculated in four regions of interest determined on soft tissue areas and compared by two-way ANOVA.Results:The ballistic gelatin showed subjective scores ranging from good to excellent for all parameters evaluated. There was no significant difference in the mean and SD values of the grey values between ballistic gelatin and the gold standard groups for all levels (p > 0.05). Higher SD values were observed in the occlusal level for both groups (p < 0.05).Conclusions:Ballistic gelatin has visual and objective similarity with the gold standard. Thus, the ballistic gelatin is a promising material capable of simulating soft tissues in CBCT images.     

Machine learning methods applied to audit of surgical margins after curative surgery for head and neck cancer

Most surgical specialties have attempted to address concerns about the unfair comparison of outcomes by ‘risk-adjusting’ data to benchmark specialty-specific outcomes that are indicative of quality of care. We explore the ability to predict for positive margin status so that effective benchmarking that will account for complexity of case mix is possible. A dataset of care episodes recorded as a clinical audit of margin status after surgery for head and neck squamous cell carcinoma (n=1316) was analysed within the Waikato Environment for Knowledge Analyisis (WEKA) machine learning programme. The outcome was a classification model that can predict for positivity of tumour margins (defined as less than 1mm) using data on preoperative demographics, operations, functional status, and tumour stage. Positive resection margins of less than 1mm were common, and varied considerably between treatment units (19%-29%). Four algorithms were compared to attempt to risk-adjust for case complexity. The 'champion' model was a Naïve Bayes classifier (AUROC 0.72) that suggested acceptable discrimination. Calibration was good (Hosmer-Lemershow goodness-of-fit test p=0.9). Adjusted positive margin rates are presented on a funnel plot. Subspecialty groups within oral and maxillofacial surgery are seeking metrics that will allow for meaningful comparison of the quality of care delivered by surgical units in the UK. To enable metrics to be effective, we argue that they can be modelled so that meaningful benchmarking, which takes account of variation in complexity of patient need or care, is possible.     

Brushing associated with oral irrigation in maintaining implants and overdentures hygiene – a randomized clinical trial

Odontology - Evaluate, through a randomized clinical trial, the efficacy of brushing associated with oral irrigation in maintaining implant and overdenture hygiene. Thirty-eight participants, who...     

Access to original canal trajectory after deviation and perforation with guided endodontic assistance

Procedural accidents are eventualities that may occur during endodontic treatment because of lack of attention to detail or even unforeseeable situations. Knowledge of the root canal anatomy and its variations is a prerequisite for successful endodontic treatment. This case report describes an endodontic treatment where there was an intercurrence, generating deviation and perforation, which was solved with the aid of guided endodontics. A 37 years old, ASA1, was referred to the clinic for localisation and treatment of a calcified canal of the second right upper premolar. The tomographic images revealed the presence of only one canal and deviation with apical perforation. With the help of CBCT and CAD/CAM, it was possible to perform the guided access technique even after deviation and root perforation. Once again, this technique proved to be safe and predictable, allowing for a favourable prognosis in the long term.     

Reliability and validity of a new classification of MIH based on severity

To describe a new molar-incisor hypomineralization (MIH) severity scoring system (MIH-SSS) that focuses on the defects’ severity and to assess the sy