Cone-beam Computed Tomography Images Research Articles

Changes in mandibular width and frontal-lower facial profile after orthognathic surgery using sagittal split ramus osteotomy with removal of internal bone interference in patients with class III skeletal malocclusion

BackgroundThe purpose of this study is to analyze changes in mandibular width and frontal view ramus inclination using cone beam CT in patients with skeletal class III malocclusion who underwent BSSRO, with the removal of bone interference between segments. MethodsFor all 20 subjects, cone-beam CT imaging was performed prior to surgery (T1), immediately post-surgery (T2), and 6 months after surgery (T3). Reorientation was performed using R2GATE software (MegaGen, Seoul, Korea). The gonion and antegonial notch were used as reference points in the sagittal view, and the most lateral point of the condyle head was used as the reference point in the frontal view. All measurements were recorded in the frontal view.ResultsInter-gonial width decreased by 2.64 mm at T3-T2 (P < .001) and by 2.58 mm at T3-T1 (P < .05). Inter-antegonial width decreased by 1.75 mm at T3-T2 (P < .05) and by 3.5 mm at T3-T1 (P < .001). In the frontal view, the right ramus inclination based on the gonion increased by 2.07° at T3-T1 (P < .05). The left ramus inclination based on gonion increased by 2.45° at T2-T1 (P < .05) and by 3.94° at T3-T1 (P < .001). The right ramus inclination based on antegonial notch increased by 2.35° at T2-T1 (P < .05) and by 3.04° at T3-T1 (P < .01). The left ramus inclination based on antegonial notch increased by 2.73° at T2-T1 (P < .001) and by 3.18° at T3-T1 (P < .001).ConclusionsDuring bilateral sagittal split osteotomy, removing bone interference between the proximal and distal segments results in a reduction of postoperative mandibular width and an increase in frontal view ramus inclination.

Enhancing Cone-Beam CT Image Quality in TIPSS Procedures Using AI Denoising.

Purpose: This study evaluates a deep learning-based denoising algorithm to improve the trade-off between radiation dose, image noise, and motion artifacts in TIPSS procedures, aiming for shorter acquisition times and reduced radiation with maintained diagnostic quality. Methods: In this retrospective study, TIPSS patients were divided based on CBCT acquisition times of 6 s and 3 s. Traditional weighted filtered back projection (Original) and an AI denoising algorithm (AID) were used for image reconstructions. Objective assessments of image quality included contrast, noise levels, and contrast-to-noise ratios (CNRs) through place-consistent region-of-interest (ROI) measurements across various critical areas pertinent to the TIPSS procedure. Subjective assessments were conducted by two blinded radiologists who evaluated the overall image quality, sharpness, contrast, and motion artifacts for each dataset combination. Statistical significance was determined using a mixed-effects model (p ≤ 0.05). Results: From an initial cohort of 60 TIPSS patients, 44 were selected and paired. The mean dose-area product (DAP) for the 6 s acquisitions was 5138.50 ± 1325.57 µGy·m2, significantly higher than the 2514.06 ± 691.59 µGym2 obtained for the 3 s series. CNR was highest in the 6 s-AID series (p < 0.05). Both denoised and original series showed consistent contrast for 6 s and 3 s acquisitions, with no significant noise differences between the 6 s Original and 3 s AID images (p > 0.9). Subjective assessments indicated superior quality in 6 s-AID images, with no significant overall quality difference between the 6 s-Original and 3 s-AID series (p > 0.9). Conclusions: The AI denoising algorithm enhances CBCT image quality in TIPSS procedures, allowing for shorter scans that reduce radiation exposure and minimize motion artifacts.

Prevalence of Incidental Findings and Assessment of Maxillary Sinus Pathologies and Dental Diseases Using Cone-Beam Computed Tomography (CBCT) in the Tamil Nadu Population: A Retrospective Study.

Background Cone-beam computed tomography (CBCT), a cross-sectional imaging technique, is valuable for clinical diagnosis and creating effective treatment plans. CBCT can precisely examine the connection between the maxillary sinuses and the maxillary root apices. Oral radiologists must be aware of all potential incidental findings and should be diligent in thoroughly identifying and assessing possible underlying diseases. Aim To determine the prevalence of incidental maxillary sinus pathologies and their relationship to dental diseases. Materials and methods In the present retrospective study, CBCT scans from 300 subjects (encompassing 600 right and left maxillary sinuses), aged 18 to 70, were gathered from various CBCT centers to represent the Tamil Nadu population. The CBCT images were analyzed using proprietary software, which provided both a panoramic reconstruction view and multiplanar reformation modules, including axial, sagittal, and coronal slices. The entire sample size was classified as follows: Group 1, age groups of 18 to 25 years; Group 2, age groups of 26 to 35 years; Group 3, age groups of 36 to 45 years; Group 4, age groups of 46 to 55 years; Group 5, age groups of 56 to 65 years; Group 6, age groups of 66 to 70 years. The prevalence of incidental maxillary sinus findings was analyzed, and their relationship with periapical abscess, periapical granuloma, periapical cyst, and breach was assessed. Results There was a prevalence of 52.05% of cases that had incidental maxillary sinus findings. Among them, 53.43% were males and 50.65% were females. Maxillary sinus pathologies were more common in individuals between 46 and 55 years, i.e., Group 4. In 300 datasets, the frequency of incidental maxillary sinus findings on the right is 21.33%, on the left is 24%, in both is 6.67%, and absent in 48% of the cases. Mucosal thickening was observed in 30% of cases with a periapical abscess, 19.52% with a periapical granuloma, 25% with a periapical cyst, and 51.79% with a breach. Polypoidal mucosal thickening was present in 32.50% of cases with a periapical abscess, 13.79% with a periapical granuloma, 50% with a periapical cyst, and 23.21% with a breach. Opacification occurred in 37.50% of cases with a periapical abscess, 20.69% with a periapical granuloma, 25% with a periapical cyst, and 25% with a breach. Conclusion Dental professionals should have a bird's-eye view in treating chronic odontogenic infections close to the maxillary sinus. Early diagnosis and prompt treatment of odontogenic infections help prevent maxillary sinus pathologies.

Evaluation of maxillary sinus volume by cone beam computed tomography in asthma patients

ObjectiveThe study aims to evaluate maxillary sinus volume in asthmatic patients. MethodsPatients who applied to the faculty of dentistry between January 2014 and December 2023 were retrospectively screened. Cone beam computed tomography images of 312 maxillary sinuses (156 people) consisting of asthmatic patients and controls were included in the study. Radiographic images in the study were obtained with a Newtom 5 G (FP, Quantitative Radiology, Verona, Italy) cone beam computed tomography device. SimPlant Pro 16 program (Materialise NV, Leuven, Belgium) measured maxillary sinus volumes. SPSS 25.0 software (IBM SPSS, Inc.) was used to analyze statistical data. ResultsAs a result of the tests, the right and left maxillary sinus volumes of asthmatic patients were significantly smaller than the control group.No significant relationship was found between age and maxillary sinus in both the study and control groups. Gender distribution was equal between the groups.There was no significant difference between the groups in terms of age. No significant difference was found between the right and left maxillary sinus volumes. ConclusionConsidering that there is no other study in the literature measuring maxillary sinus volume in asthmatic patients, this study may guide future research.

Accuracy of deep learning-based upper airway segmentation

IntroductionIn orthodontic treatments, accurately assessing the upper airway volume and morphology is essential for proper diagnosis and planning. Cone beam computed tomography (CBCT) is used for assessing upper airway volume through manual, semi-automatic, and automatic airway segmentation methods. This study evaluates upper airway segmentation accuracy by comparing the results of an automatic model and a semi-automatic method against the gold standard manual method. Materials and methodsAn automatic segmentation model was trained using the MONAI Label framework to segment the upper airway from CBCT images. An open-source program, ITK-SNAP, was used for semi-automatic segmentation. The accuracy of both methods was evaluated against manual segmentations. Evaluation metrics included Dice Similarity Coefficient (DSC), Precision, Recall, 95% Hausdorff Distance (HD), and volumetric differences. ResultsThe automatic segmentation group averaged a DSC score of 0.915±0.041, while the semi-automatic group scored 0.940±0.021, indicating clinically acceptable accuracy for both methods. Analysis of the 95% HD revealed that semi-automatic segmentation (0.997±0.585) was more accurate and closer to manual segmentation than automatic segmentation (1.447±0.674). Volumetric comparisons revealed no statistically significant differences between automatic and manual segmentation for total, oropharyngeal, and velopharyngeal airway volumes. Similarly, no significant differences were noted between the semi-automatic and manual methods across these regions. ConclusionIt has been observed that both automatic and semi-automatic methods, which utilise open-source software, align effectively with manual segmentation. Implementing these methods can aid in decision-making by allowing faster and easier upper airway segmentation with comparable accuracy in orthodontic practice.

Evaluation of the presence of nasal septum deviations in individuals with different skeletal malocclusions

OBJECTIVE: The aim of this study was to evaluate the presence of deviated septum in individuals with different skeletal malocclusions. MATERIALS AND METHOD: The study was performed on cone beam computed tomography images of a total of 159 individuals, 53 in each Class I, Class II, and Class III skeletal malocclusion. Nasal septum deviation was analysed in three groups as mild (type 1; &amp;lt;9°), moderate (type 2; 9-15°), and severe (type3; &amp;gt;15°). In addition, the presence of concha bullosa in different skeletal malocclusions was evaluated. The data were analyzed statistically using the Chi-square test and One-way Analysis of Variance (ANOVA). The relationship betweenskeletal malocclusions and types of deviation was determined using Cramèr’s V analysis. RESULTS: There are statistically significant differences between type 2 and type 3 in individuals with Class I skeletal malocclusion and between type 1 and type 3 in individuals with Class II skeletal malocclusion (p=0.046; p&amp;lt;0.01 respectively). In individuals with Class III skeletal malocclusion, no difference was observed between deviation types. In addition, a weak correlation was found between skeletal malocclusion and deviation types (Cramèr’s V = 0.213). No significant difference was observed between the groups in terms of concha bullosa and deviation side (p=0.215). CONCLUSION: Although a significant difference was observed in terms of deviation type in individuals with Class I and II skeletal malocclusion, a weak correlation was observed between the increase in deviation severity and skeletal malocclusions.

Automated condylar seating assessment using a deep learning-based three-step approach

ObjectivesIn orthognatic surgery, one of the primary determinants for reliable three-dimensional virtual surgery planning (3D VSP) and an accurate transfer of 3D VSP to the patient in the operation room is the condylar seating. Incorrectly seated condyles would primarily affect the accuracy of maxillary-first bimaxillary osteotomies as the maxillary repositioning is dependent on the positioning of the mandible in the cone-beam computed tomography (CBCT) scan. This study aimed to develop and validate a novel tool by utilizing a deep learning algorithm that automatically evaluates the condylar seating based on CBCT images as a proof of concept.Materials and methodsAs a reference, 60 CBCT scans (120 condyles) were labeled. The automatic assessment of condylar seating included three main parts: segmentation module, ray-casting, and feed-forward neural network (FFNN). The AI-based algorithm was trained and tested using fivefold cross validation. The method’s performance was evaluated by comparing the labeled ground truth with the model predictions on the validation dataset.ResultsThe model achieved an accuracy of 0.80, positive predictive value of 0.61, negative predictive value of 0.9 and F1-score of 0.71. The sensitivity and specificity of the model was 0.86 and 0.78, respectively. The mean AUC over all folds was 0.87.ConclusionThe innovative integration of multi-step segmentation, ray-casting and a FFNN demonstrated to be a viable approach for automating condylar seating assessment and have obtained encouraging results.Clinical relevanceAutomated condylar seating assessment using deep learning may improve orthognathic surgery, preventing errors and enhancing patient outcomes in maxillary-first bimaxillary osteotomies.

Multi-scale anatomical regularization for domain-adaptive segmentation of pelvic CBCT images.

Cone beam computed tomography (CBCT) image segmentation is crucial in prostate cancer radiotherapy, enabling precise delineation of the prostate gland for accurate treatment planning and delivery. However, the poor quality of CBCT images poses challenges in clinical practice, making annotation difficult due to factors such as image noise, low contrast, and organdeformation. The objective of this study is to create a segmentation model for the label-free target domain (CBCT), leveraging valuable insights derived from the label-rich source domain (CT). This goal is achieved by addressing the domain gap across diverse domains through the implementation of a cross-modality medical image segmentationframework. Our approach introduces a multi-scale domain adaptive segmentation method, performing domain adaptation simultaneously at both the image and feature levels. The primary innovation lies in a novel multi-scale anatomical regularization approach, which (i) aligns the target domain feature space with the source domain feature space at multiple spatial scales simultaneously, and (ii) exchanges information across different scales to fuse knowledge from multi-scaleperspectives. Quantitative and qualitative experiments were conducted on pelvic CBCT segmentation tasks. The training dataset comprises 40 unpaired CBCT-CT images with only CT images annotated. The validation and testing datasets consist of 5 and 10 CT images, respectively, all with annotations. The experimental results demonstrate the superior performance of our method compared to other state-of-the-art cross-modality medical image segmentation methods. The Dice similarity coefficients (DSC) for CBCT image segmentation results is %, and the average symmetric surface distance (ASSD) is . Statistical analysis confirms the statistical significance of the improvements achieved by ourmethod. Our method exhibits superiority in pelvic CBCT image segmentation compared to its counterparts.

Modified Surgical Guide for Root Sectioning in the Socket Shield Technique-In Vitro Study.

This in vitro study aimed to evaluate the effectiveness of a printed surgical guide for root sectioning in the socket-shield technique. A typodont model of the maxilla with augmented filler was used for the upper right central incisor, and CBCT images were obtained. Two types of vertical root sectioning guides were tested: one with a buccal sleeve (Group A, n = 10) and another with a buccal orientation slit (Group B, n = 10). Control group (n = 10) performed freehand cuts on printed models. After crown cutting with diamond burs, root sectioning was conducted using an ultrasonic tip with the guides. Mean and standard deviation were calculated for the remaining root length, width, and volume. Data were analyzed using the Kruskal-Wallis test and Dunn's post hoc test. Statistical analysis showed significant differences in root width between the control group (2.0 mm) and both Groups A (2.655 mm; p < 0.0001) and B (2.460 mm; p < 0.0001). Group B (5.585 mm) also showed a significant difference in root remnant compared with the control (13.880 mm; p < 0.0043). Groups A and B did not differ significantly from each other. The socket shield technique is safe and effective, this study aimed to add improvements through the introduction of surgical guides, facilitating the root section, which is the critical phase so that the parameters that are requisite for success are achieved in terms of width, length, and volume of the root remnant. Both guided techniques effectively facilitated root sectioning, maintaining satisfactory root thickness and length. Sectioning the root is challenging for the correct angulation to remove the palatal portion in the socket shield technique. Therefore, modifying this technique with the use of the presented guides, it is possible to prevent damage to the soft tissues and to plan the surgery with the help of cone beam computed tomography (CBCT) scans to remove the root apex and maintain the root remnant with a thickness greater than 1.5 mm, making the socket-shield technique more predictable.

The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study.

Artificial intelligence (AI) models are being increasingly studied for the detection of variations and pathologies in different imaging modalities. Nasal septal deviation (NSD) is an important anatomical structure with clinical implications. However, AI-based radiographic detection of NSD has not yet been studied. This research aimed to develop and evaluate a real-time model that can detect probable NSD using cone beam computed tomography (CBCT) images. Coronal section images were obtained from 204 full-volume CBCT scans. The scans were classified as normal and deviated by 2 maxillofacial radiologists. The images were then used to train and test the AI model. Mask region-based convolutional neural networks (Mask R-CNNs) comprising 3 different backbones-ResNet50, ResNet101, and MobileNet-were used to detect deviated nasal septum in 204 CBCT images. To further improve the detection, an image preprocessing technique (contrast enhancement [CEH]) was added. The best-performing model-CEH-ResNet101-achieved a mean average precision of 0.911, with an area under the curve of 0.921. The performance of the model shows that the model is capable of detecting nasal septal deviation. Future research in this field should focus on additional preprocessing of images and detection of NSD based on multiple planes using 3D images.

Deep learning method for predicting weekly anatomical changes in patients with nasopharyngeal carcinoma during radiotherapy.

Patients may undergo anatomical changes during radiotherapy, leading to an underdosing of the target or overdosing of the organs at risk (OARs). This study developed a deep-learning method to predict the tumor response of patients with nasopharyngeal carcinoma (NPC) during treatment. This method can predict the anatomical changes of a patient. The participants included 230 patients with NPC. The data included planning computed tomography (pCT) and routine cone-beam CT (CBCT) images. The CBCT image quality was improved to the CT level using an advanced method. A long short-term memory network-generative adversarial network (LSTM-GAN) is proposed, which can harness the forecasting ability of LSTM and the generation ability of GAN. Four models were trained to predict the anatomical changes that occurred in weeks 3-6 and named LSTM-GAN-week 3 to LSTM-GAN-week 6. The pCT and CBCT were used as input, and the tumor target volumes (TVs) and OARs were delineated on the predicted and real images (ground truth). Finally, the models were evaluated using contours and dosimetry parameters. The proposed method predicted the anatomical changes, with a dice similarity coefficient above 0.94 and 0.90 for the TVs and surrounding OARs, respectively. The dosimetry parameters were close between the prediction and ground truth. The deviations in the prescription, minimum, and maximum doses of the tumor targets were below 0.5Gy. For serial organs (brain stem and spinal cord), the deviations in the maximum dose were below 0.6Gy. For parallel organs (bilateral parotid glands), the deviations in the mean dose were below 0.8Gy. The proposed method can predict the tumor response to radiotherapy in the future such that adaptation can be scheduled on time. This study provides a proactive mechanism for planning adaptation, which can enable personalized treatment and save clinical time by anticipating and preparing for treatment strategy adjustments.

Automated classification of mandibular canal in relation to third molar using CBCT images

Background Dental radiology has significantly benefited from cone-beam computed tomography (CBCT) because of its compact size and low radiation exposure. Canal tracking is an important application of CBCT for determining the relationship between the inferior alveolar nerve and third molar. Usually, canal tacking is performed manually, which takes a lot of time. This study aimed to develop an artificial intelligence (AI) model to automate classification of the mandibular canal in relation to the third molar. Methods This retrospective study was conducted using 434 CBCT images. 3D slicer software was used to annotate and classify the data into lingual, buccal, and inferior categories. Two convolution neural network models, AlexNet and ResNet50, were developed to classify this relationship. The study included 262 images for training and 172 images for testing, with the model performance evaluated by sensitivity, precision, and F1 score. Results The performance of the two models was evaluated using a 3 × 3 confusion matrix, with the data categorized into 3 clases: lingual, buccal, and inferior. The mandibular canal and third molar have a close anatomical relationship, highlighting the need for precise imaging in dental and surgical settings. To accurately classify the mandibular canal in relation to the third molar, both AlexNet and ResNet50 demonstrated high accuracy, with F1 scores ranging from 0.64 to 0.92 for different classes, with accuracy of 81% and 83%, respectively, for accurately classifying the mandibular canal in relation to the third molar. Conclusion The present study developed and evaluated AI models to accurately classify and establish the relationship between the mandibular canal and third molars using CBCT images with a higher accuracy rate.

Anatomical Factors Affecting the Complexity of Maxillary Sinus Augmentation in Saudi Patients: A Cone Beam Computed Tomography (CBCT) Study.

This study aims to evaluate the anatomical variations of the maxillary sinus and determine the difficulty score of maxillary sinus augmentation (MSA) in Saudi patients seeking dental implant rehabilitation of the posterior maxilla using cone beam computed tomography (CBCT). CBCT records of dental patients seeking treatment at a University Dental Teaching Hospital between 2019 and 2023 were retrospectively analyzed. Measurements were obtained from CBCT images, including membrane thickness, sinus width, buccal bone thickness, presence of maxillary sinus septa, residual alveolar ridge height, angle of the buccolingual sinus wall, and the presence of the alveolar antral artery (AAA). The difficulty score for MSA was determined based on these anatomical factors. A total of 107 maxillary sinuses in 86 subjects were evaluated. The average membrane thickness was 2.23 mm, with males showing significantly higher thickness than females. Sinus septa were found in 54 (50.5%) sinuses, with 18 (17%) of sinuses having interfering septa. Twenty-three (21.5%) sinuses had a residual alveolar ridge height of less than 4 mm. The average angle of the buccolingual sinus wall was 79.39°, indicating a high prevalence of wide-shaped sinuses. The average sinus width was 14.09 mm, with 55 sinuses (51.4%) less than 15 mm. The average buccal bone thickness was 1.07 mm, in29 (27%) sinuses, the thickness was more than 2 mm. AAA was visualized in 60 (56%) of sinuses, with 45 (42%) of sinuses having AAA interfering with the MSA window. In this study, most sinuses were classified as simple or moderate difficulty, with higher membrane thickness, presence of septa, and AAA being the significant risk factors for complications. These findings provide valuable insights for implant surgeons in Saudi patients seeking dental implant rehabilitation of the posterior maxilla, enabling them to anticipate and minimize potential complications during MSA procedures.

Morphometric Evaluation of the Maxillary Arch Using Cone Beam Computed Tomography

Objective: Anatomically, a dental arch is a result of natural teeth being positioned on the alveolar bone. The purpose of this study is to assess the morphological characteristics of various maxillary arch types using Cone Beam Computed Tomography (CBCT) and ascertain their prevalence. Methods: This retrospective study analyzed 200 randomly selected maxillary CBCT images from patients aged 18–65. Measurements were interpreted using various planes, and maxillary arch forms were classified according to the House dental arch classification in the axial region of the CBCT images. Results: Patients over 45 years old had significantly higher mean canine-palate and first molar-palate measurements compared to younger groups, particularly the 18–25 age group, which showed a higher hard palate–anterior mean than the 25–35 group. Class II cases had a significantly higher mean canine-palate than Class I and III cases. Class II cases also exhibited higher first molar-palate and canine-anterior means compared to Class I and III. In contrast, Class I cases had a higher canine-canine mean than the other classes. Conclusion: Morphologic measurements are crucial for guiding specialists in diagnosis and treatment, enhancing the ease and accuracy of clinical practice.

Evaluation of root and root canal morphology in maxillary premolar teeth: A cone-beam computed tomography study using two classification systems in a Japanese population

Background/purposeInadequate knowledge increases the risk of overlooking root canals and causing procedural errors that can lead to root canal treatment failure. This study aimed to analyze the root and root canal morphology in maxillary premolar teeth using two classification systems assessed by cone-beam computed tomography (CBCT) in a large Japanese population. Materials and methodsThis study evaluated 1443 maxillary teeth including 726 first and 717 s premolars, from 642 Japanese individuals aged 16–84 years. CBCT images were analyzed according to the Vertucci and Ahmed classification systems, focusing on the root and root canal morphology. Variations were analyzed based on gender and age using the chi-square test (P < 0.05). ResultsMaxillary first premolars were predominantly single-rooted (64.9%) and had two root canals (86.2%). Females exhibited a higher prevalence of single-rooted (71.0%) compared to males (49.8%) (P < 0.05). Additionally, single root canals occurred significantly more frequently in females (14.0%) than in males (7.5%) (P < 0.05). Maxillary second premolars mostly had a single root (97.8%) with a single root canal (72.1%). The frequency of second premolars with two root canals significantly increased with age (P < 0.001). The three-rooted configuration occurred in 1.2% and 0.3% of the first and second premolars, respectively. ConclusionMaxillary premolars in a Japanese population showed significant anatomical variations, highlighting the importance of understanding anatomical characteristics specific to age and gender. Ahmed's system provides a more comprehensive description of the morphological variations.

Evaluation of the Role of Cone-Beam Computed Tomography as an Adjunctive Radiographic Modality to Enhance the Surgical Outcomes in Obstructive Sleep Apnoea Patients

Purpose: To evaluate the integration of Cone-beam Computed Tomography (CBCT) as an auxiliary radiographic modality for airway assessment to pinpoint blockage locations and improve Obstructive Sleep Apnea (OSA) surgical results. Material and methods: Thirty-eight OSA patients were selected from the ENT outpatient clinic, Faculty of Medicine, Mansoura University, and divided into two equal groups. After performing polysomnography and recording the Apnea Hypoapnea Index (AHI), Drug-Induced Sleep Endoscopy (DISE) was carried out for both groups. CBCT imaging was performed for group (1) patients only. Afterward, the otolaryngologist selected the suitable surgical procedure for all patients. Post-surgical results were assessed six months after surgery. Another polysomnography was used to compare the postoperative AHI value to the preoperative value. At least a 50% reduction in the postoperative value was considered a conventional surgical success. Results: A significant difference in AHI values before and after the operation between both groups was shown. Group (1) reduced by 55.7% more than group (2), which reduced by 30.7%. There was no statistically significant difference between the success rates of the two groups. Conclusion: CBCT quantifies airway obstruction preoperatively, serving DISE, and thus improving OSA surgical success. Better collapse extent assessment using CBCT improves results and prospects. Future studies should explore specialists' CBCT use with larger sample sizes and a wider spectrum of OSA patients with different AHI levels.

Comparative analysis of 3D tomography based soft tissue rendering and Proface facial scanning systems in orthognathic surgery

PurposeThis study aimed to investigate the linear and angular differences in the nasolabial soft tissue in patients who underwent bimaxillary orthognathic surgery using two different three-dimensional imaging methods. Furthermore, the advantages, disadvantages, and limitations of these methods were determined after comparing the data obtained from the imaging methods used in the study. Materials and MethodsPreoperative (T0) and 6-months postoperative (T1) cone-beam computed tomography (CBCT) and three-dimensional facial scanning (3DFS) data from 22 patients who underwent maxillary advancement surgery were examined. The DICOM (Digital Imaging and Communications in Medicine) data (CBCT group) and “.obj” format images (3DFS group) of the patients were analyzed using Dolphin software (Dolphin Imaging®, Version 12, Chatsworth, CA, USA). The linear and angular soft tissue measurements were calculated after determining the reference anatomical landmarks for both groups. ResultsMeasurements with CBCT and 3DFS imaging methods were compared at T0, T1, and all measurements (T0+T1). No statistically significant difference was observed between the CBCT and 3DFS groups for five measurements performed at T0 and T0+T1, but statistically significant differences were observed between the groups for the other seven measurements. There was no statistically significant difference between the CBCT and 3DFS groups for six measurements at T1, but there were statistically significant differences between the groups for the other six measurements. After reviewing the postoperative differences in the nasolabial soft tissue, a statistically significant increase in four linear and one angular measurement in the 3DFS group was observed, and there was a statistically significant increase in two linear and two angular measurements in the CBCT group. Upon comparison of postoperative differences in soft tissue alterations, no statistically significant difference between the 3DFS group and the CBCT group were observed in any of the soft tissue measurements. ConclusionOrthognathic surgery has significant effects on nose width and upper lip morphology. Although both 3DFS and CBCT methods can be used to evaluate such effects, the results of the present study revealed differences in sensitivity and limitations between the two methods. Thus, surgical outcomes should be evaluated in consideration of the abovementioned parameters.

Prevalence of middle mesial canal in mandibular molars: A cross-sectional cone-beam computed tomography study.

The first tooth to erupt is the first mandibular molar, which is the tooth with the highest number of retreatments. Several factors are responsible for the failure of the endodontic success and one of the most important being the particular pulp anatomy of each tooth. To aim was determine the prevalence of the middle mesial (MM) canal in first mandibular molars and to study if there are predisposing factors to the presence of this canal by retrospectively analyzing cone-beam computed tomography (CBCT) images in vivo. CBCT images of 100 patients were selected. A total of 206 first mandibular molars were examined. The CBCTs were analysed using Careastream CS 3D imaging software. Findings of MM canals were recorded along the variables sex and left/right side. Prevalence was compared using the Chi-square test (P< 0.05) and Cramer's V was used to indicate the effect size between the variables. Of the 206 teeth analysed, the prevalence of MM canals was 33.11% (49 teeth). There was no statistically significant difference between sex and prevalence of the MM canal. There was a significant difference between sex and the distance between the mesial canals was found, being the most common range in women was 1-2 mm (35.8%) and 2-3 mm (51%) in men. The most common range of distance between the mesial canals where the MM canal was localized was 3-4 mm (50%), being statistically significant (p<0,05). This cross-sectional study showed a high prevalence of MM canals (33.11%) in first mandibular molars. The prevalence of the MM canal was significantly higher when the distance between mesiobucal and mesiolingual canals was 3-4mm. This knowledge let direct the clinicians in locating MM canal for improving endodontic prognosis. Key words:Cone-beam computed tomography, middle mesial canal, prevalence.

A Randomized Controlled Trial on the Timing of Soft-Tissue Augmentation in Immediate Implant Placement: Hard-Tissue Changes and Clinical Outcome.

To assess the impact of the timing of soft-tissue augmentation (STA) on mean buccal bone changes following immediate implant placement (IPP) in the anterior maxilla. Patients with a failing tooth and intact buccal bone wall in the anterior maxilla (15-25) were enrolled in this randomized controlled trial. Following single IIP and socket grafting, they were randomly allocated to the control group (immediate STA performed during the same surgical procedure) or the test group (delayed STA performed 3 months later). Implants were placed with a surgical guide and immediately restored with an implant-supported provisional crown. Changes in bone dimensions were assessed using superimposed CBCT images taken prior to surgery and at 1-year follow-up. Clinical outcomes were registered at 1-year follow-up. Twenty patients were randomized to each group (control: 16 females, 4 males, mean age 57.6; test: 9 females, 11 males, mean age 54.2). Ten patients in the control group and 13 patients in the test group had a thick bone wall phenotype. Estimated marginal mean horizontal buccal bone loss at 1 mm below the implant shoulder was -0.553 and -0.898 mm for the control and test group, respectively. The estimated mean difference of 0.344 mm in favour of the control group was not significant (95% CI: -0.415 to 1.104; p = 0.363). Also at all other horizontal and vertical levels, no significant differences could be observed between the groups. The combination of socket grafting and STA enabled counteraction of any buccal soft-tissue loss (≥ 0 mm) at 1 mm below the implant shoulder in 82% of the patients in the control group and in 75% of the patients in the test group (p = 1.000). The clinical outcome was favourable in both groups, yet implants in the control group demonstrated slightly less marginal bone loss (median difference 0.20 mm; 95% CI: 0.00-0.44; p = 0.028). In patients with an intact and mainly thick buccal bone wall in the anterior maxilla, the timing of STA following IIP had no significant impact on mean buccal bone loss. ClinicalTrials.gov identifier: NCT05537545.

Preoperative Cone Beam Computed Topography Assessment of Maxillary Sinus Variations in Dental Implant Patients.

The study included a cohort of 200 patients recommended for CBCT as part of their preoperative evaluation. The methodology involved detailed CBCT image analysis to identify and document various anatomical variations due to pneumatization, exostosis, hypoplasia, polyps, cysts, foreign bodies, and anthroliths within the maxillary sinus. Pneumatization was the most common variation, present in 77.5% of subjects. Polypoid lesions were found in 17.5% of patients, with a higher prevalence in younger age groups (57.1% in ages 20-35). Cysts and polyps affected 17.5% of subjects, predominantly males (65.7%). Anthroliths were observed in a minimal percentage (2%), and foreign bodies were found in 1.5% of the patients. Positive correlations were observed between the patient's age and both mucosal thickness and polypoid lesions and between the patient's gender and bone thickening (p-values < 0.05). The study concluded that CBCT is essential in the preoperative assessment of the maxillary sinus in dental implant candidates due to its superior imaging capabilities, allowing for the identification of critical anatomical variations and pathologies. This thorough evaluation is imperative to ensure the success of implant placement and to mitigate potential complications. However, further research with larger, more diverse populations is recommended to confirm these findings.