Use of dentomaxillofacial cone beam computed tomography in dentistry

Can convolutional neural networks identify external carotid artery calcifications?

OBJECTIVE

We developed and evaluated the accuracy and reliability of a convolutional neural network (CNN) in detecting external carotid artery calcifications (ECACs) in cone beam computed tomography scans.

STUDY DESIGN

Using TensorFlow, we developed a program to identify calcification in 427 cone beam computed tomography scans evaluated to determine the presence of ECACs. We compared the results to the findings of a human evaluator. Using an 80:20 training-to-validation ratio, we calculated the k-fold cross-validation accuracy of the initial dataset and extrapolated the F1 score and Matthews Correlation Coefficient.

RESULTS

We calculated a k-fold cross-validation accuracy of 76%, with a recall and precision of 66% and 79%, respectively, and a combined F1 score of 0.72. We extrapolated a Matthews correlation coefficient of 0.53, showing a strong balance between confusion matrix categories.

CONCLUSION

Our CNN model can reliably identify ECACs in cone beam computed tomography scans.

Cone-Beam Computerized Tomography Evaluation of the Relationship between Orthodontic Vertical Direction Parameters and the Distance from the Apex of the Upper Central Tooth to the Nasal Floor and Anterior Nasal Spine

The aim of this study was to examine the relationship between the vertical cephalometric values and the distance from the apex tip of the upper central tooth (U1A) to the anterior nasal spine (ANS) and nasal floor (NF) using cone-beam computerized tomography (CBCT). One hundred and twenty-two patients who applied to the Department of Orthodontics between January 2011 and June 2019 were included. The distances between the U1A and the NF and ANS were measured using CBCT. Statistical significance was considered as p < 0.05. Of the 122 individuals, 73.8% (n = 90) were female and 26.2% (n = 32) were male, with a mean age of 22.8 ± 3.3 years. A statistically significant moderate positive correlation was found between the mean NF-U1A values and the N-Me, ANS-Me, ANS-Gn, S-Go, and N-ANS measurements (p < 0.01). A statistically significant positive correlation was found between the mean ANS-U1A values and the Ar-Go-Me, total posterior angles, N-Me, SN/GoGn and Y-axis angle, ANS-Me, and ANS-Gn measurements (p < 0.01). The distance from the U1A to the ANS and NF was related to the orthodontic vertical direction parameters. The ANS-U1A and NF-U1A distances can serve as reference points for identifying the orthodontic vertical growth pattern from CBCT scans.

Evaluation of an artificial intelligence system for the diagnosis of apical periodontitis on digital panoramic images

Aims

The aim of the present study was to evaluate the effectiveness of an artificial intelligence (AI) system in the detection of roots with apical periodontitis (AP) on digital panoramic radiographs.

Materials and Methods

Three hundred and six panoramic radiographs containing 400 roots with AP (an equal number for both jaws) were used to test the diagnostic performance of an AI system. Panoramic radiographs of the patients were selected with the terms 'apical lesion' and 'apical periodontitis' from the archive and then with the agreement of two oral and maxillofacial radiologists. The radiologists also carried out the grouping and determination of the lesion borders. A deep learning (DL) model was built and the diagnostic performance of the model was evaluated by using recall, precision, and F measure.

Results

The recall, precision, and F-measure scores were 0.98, 0.56, and 0.71, respectively. While the number of roots with AP detected correctly in the mandible was 169 of 200 roots, it was only 56 of 200 roots in the maxilla. Only four roots without AP were incorrectly identified as those with AP.

Conclusions

The DL method developed for the automatic detection of AP on digital panoramic radiographs showed high recall, precision, and F measure values for the mandible, but low values for the maxilla, especially for the widened periodontal ligament (PL)/uncertain AP.

Validation of bone mineral density measurement using quantitative CBCT image based on deep learning

The bone mineral density (BMD) measurement is a direct method of estimating human bone mass for diagnosing osteoporosis, and performed to objectively evaluate bone quality before implant surgery in dental clinics. The objective of this study was to validate the accuracy and reliability of BMD measurements made using quantitative cone-beam CT (CBCT) image based on deep learning by applying the method to clinical data from actual patients. Datasets containing 7500 pairs of CT and CBCT axial slice images from 30 patients were used to train a previously developed deep-learning model (QCBCT-NET). We selected 36 volumes of interest in the CBCT images for each patient in the bone regions of potential implants sites on the maxilla and mandible. We compared the BMDs shown in the quantitative CBCT (QCBCT) images with those in the conventional CBCT (CAL_CBCT) images at the various bone sites of interest across the entire field of view (FOV) using the performance metrics of the MAE, RMSE, MAPE (mean absolute percentage error), R² (coefficient of determination), and SEE (standard error of estimation). Compared with the ground truth (QCT) images, the accuracy of the BMD measurements from the QCBCT images showed an RMSE of 83.41 mg/cm³, MAE of 67.94 mg/cm³, and MAPE of 8.32% across all the bone sites of interest, whereas for the CAL_CBCT images, those values were 491.15 mg/cm³, 460.52 mg/cm³, and 54.29%, respectively. The linear regression between the QCBCT and QCT images showed a slope of 1.00 and a R² of 0.85, whereas for the CAL_CBCT images, those values were 0.32 and 0.24, respectively. The overall SEE between the QCBCT images and QCT images was 81.06 mg/cm³, whereas the SEE for the CAL_CBCT images was 109.32 mg/cm³. The QCBCT images thus showed better accuracy, linearity, and uniformity than the CAL_CBCT images across the entire FOV. The BMD measurements from the quantitative CBCT images showed high accuracy, linearity, and uniformity regardless of the relative geometric positions of the bone in the potential implant site. When applied to actual patient CBCT images, the CBCT-based quantitative BMD measurement based on deep learning demonstrated high accuracy and reliability across the entire FOV.

Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography

Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies. Our aim was to propose a 3.5D U-Net to improve the performance of the U-Net in segmenting teeth on CBCT. This study retrospectively enrolled 24 patients who received CBCT. Five U-Nets, including 2Da U-Net, 2Dc U-Net, 2Ds U-Net, 2.5Da U-Net, 3D U-Net, were trained to segment the teeth. Four additional U-Nets, including 2.5Dv U-Net, 3.5Dv5 U-Net, 3.5Dv4 U-Net, and 3.5Dv3 U-Net, were obtained using majority voting. Mathematical morphology operations including erosion and dilation (E&D) were applied to remove diminutive noise speckles. Segmentation performance was evaluated by fourfold cross validation using Dice similarity coefficient (DSC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV). Kruskal-Wallis test with post hoc analysis using Bonferroni correction was used for group comparison. P < 0.05 was considered statistically significant. Performance of U-Nets significantly varies among different training strategies for teeth segmentation on CBCT (P < 0.05). The 3.5Dv5 U-Net and 2.5Dv U-Net showed DSC and PPV significantly higher than any of five originally trained U-Nets (all P < 0.05). E&D significantly improved the DSC, accuracy, specificity, and PPV (all P < 0.005). The 3.5Dv5 U-Net achieved highest DSC and accuracy among all U-Nets. The segmentation performance of the U-Net can be improved by majority voting and E&D. Overall speaking, the 3.5Dv5 U-Net achieved the best segmentation performance among all U-Nets.

Three-dimensional Analysis of Alveolar Bone With and Without Periodontitis

OBJECTIVE

The aim of this study was to investigate the alveolar bone density and thickness in Chinese participants with and without periodontitis.

METHODOLOGY

This study was retrospective and cross-sectional in nature and used cone-beam computed tomography (CBCT) to evaluate alveolar bone loss, bone density, and bone thickness around 668 mandibular molars (344 periodontally healthy teeth and 324 teeth with periodontitis). Comparative statistical tests were done related to the age, sex, tooth type, tooth side, and degree of bone loss. The significance level was set to be P < .05.

RESULTS

The alveolar bone density significantly differed between the healthy and periodontitis groups (mean difference = 24.4 Hounsfield units; P = .007). Similarly, the alveolar bone thickness of the healthy group was significantly higher than that of the periodontitis group (4.6 ± 1.8 mm compared to 4.2 ± 1.1 mm). Teeth in females demonstrated a significantly (P ˂ .001) higher bone density compared with males in both healthy and compromised groups. However, males showed a significantly (P ˂ .05) thicker bone of the teeth than females in relation to the healthy group. The alveolar bone density and thickness in both healthy and periodontitis groups significantly differed between the first and the second molars (P < .001). The alveolar bone thickness had a highly significant difference (P < .001) between the different degrees of bone loss.

CONCLUSIONS

Alveolar bone thickness and density were reduced at periodontally diseased teeth.

Criteria for the Classification of the Interradicular Septum Shape in Maxillary Molars with Clinical Importance for Prosthetic-Driven Immediate Implant Placement

The aim of this study was to use cone-beam computed tomography (CBCT) to evaluate the morphometric properties of the interradicular septum (IRS) in the maxillary molar region that may be indicative for prosthetic-driven implant placement. Following the repetitive algorithm based on the visual identification of IRS shapes, we described the following IRS shapes: arrow, boat, drop, and palatal and buccal convergence. The incidence of IRS shapes showed significant differences for the first and second maxillary molars (the highest frequency for the arrow shape, and the lowest for the drop shape) with no significant difference between the molars. The most prominent width indicative for implant placement was observed in the palatal convergence shape, whereas the height criteria were the most satisfying in the buccal convergence-shaped IRS for both molars. Apart from the parameters in the coronal view, the image analysis in the axial view revealed that IRS surface area, required for the implant placement, was the most prominent in the palatal convergence shape for the first, and boat shape for the second molars. Our results showed the benefits of CBCT diagnostics in posterior maxilla morphometric analysis. IRS shape classification may be helpful in achieving more rapid and accurate planning for interventions in this region.

Reliability of cone beam CT for morphometry of nasolabial soft tissue in patients with skeletal class III malocclusion: A qualitative and quantitative analysis

OBJECTIVE

To assess reliability of cone-beam CT (CBCT) for nasolabial soft tissue measurements in patients with skeletal class III malocclusion based on 3-dimensional (3D) facial scanner results.

METHODS

CBCT and 3D facial scan images of 20 orthognathic patients are used in this study. Eleven soft tissue landmarks and 15 linear and angular measurements are identified and performed. For qualitative evaluation, Shapiro-Wilk test and Bland-Altman plots are applied to analyze the equivalence of the measurements derived from these two kinds of images. To quantify specific deviation of CBCT measurements from facial scanner, the latter is set as a benchmark, and mean absolute difference (MAD) and relative error magnitude (REM) for each variable are also calculated.

RESULTS

Statistically significant differences are observed in regions of nasal base and lower lip vermilion between two methods. MAD value for all length measurements are less than 2 mm and for angular variables < 8°. The average MAD and REM for length measurements are 0.94 mm and 5.64%, and for angular measurements are 2.27° and 3.78%, respectively.

CONCLUSIONS

The soft tissue results measured by CBCT show relatively good reliability and can be used for 3D measurement of soft tissue in the nasolabial region clinically.

Quantitative Automated Segmentation of Lipiodol Deposits on Cone Beam CT Imaging acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach

PURPOSE

The purpose of this study was to show that a deep learning-based, automated model for Lipiodol segmentation on CBCT after cTACE performs closer to the "ground truth segmentation" than a conventional thresholding-based model.

MATERIALS & METHODS

This post-hoc analysis included 36 patients with a diagnosis of HCC or other solid liver tumor who underwent cTACE with an intra-procedural CBCT. Semi-automatic segmentation of Lipiodol were obtained. Then, a convolutional U-net model was used to output a binary mask that predicts Lipiodol deposition. A threshold value of signal intensity on CBCT was used to obtain a Lipiodol mask for comparison. Dice similarity coefficient (DSC), Mean-squared error (MSE), and Center of Mass (CM), and fractional volume ratios for both masks were obtained by comparing them to the ground truth (radiologist segmented Lipiodol deposits) to obtain accuracy metrics for the two masks. These results were used to compare the model vs. the threshold technique.

RESULTS

For all metrics, the U-net outperformed the threshold technique: DSC (0.65±0.17 vs. 0.45±0.22,p<0.001) and MSE (125.53±107.36 vs. 185.98±93.82,p=0.005). Difference between the CM predicted, and the actual CM was (15.31±14.63mm vs. 31.34±30.24mm,p<0.001), with lesser distance indicating higher accuracy. The fraction of volume present ([predicted Lipiodol volume]/[ground truth Lipiodol volume]) was 1.22±0.84vs.2.58±3.52,p=0.048 for our model's prediction and threshold technique, respectively.

CONCLUSION

This study showed that a deep learning framework could detect Lipiodol in CBCT imaging and was capable of outperforming the conventionally used thresholding technique over several metrics. Further optimization will allow for more accurate, quantitative predictions of Lipiodol depositions intra-procedurally.

Virtual Surgical Planning: Modeling from the Present to the Future

Virtual surgery planning is a non-invasive procedure, which uses digital clinical data for diagnostic, procedure selection and treatment planning purposes, including the forecast of potential outcomes. The technique begins with 3D data acquisition, using various methods, which may or may not utilize ionizing radiation, such as 3D stereophotogrammetry, 3D cone-beam CT scans, etc. Regardless of the imaging technique selected, landmark selection, whether it is manual or automated, is the key to transforming clinical data into objects that can be interrogated in virtual space. As a prerequisite, the data require alignment and correspondence such that pre- and post-operative configurations can be compared in real and statistical shape space. In addition, these data permit predictive modeling, using either model-based, data-based or hybrid modeling. These approaches provide perspectives for the development of customized surgical procedures and medical devices with accuracy, precision and intelligence. Therefore, this review briefly summarizes the current state of virtual surgery planning.

QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study

The purpose of this study was to directly and quantitatively measure BMD from Cone-beam CT (CBCT) images by enhancing the linearity and uniformity of the bone intensities based on a hybrid deep-learning model (QCBCT-NET) of combining the generative adversarial network (Cycle-GAN) and U-Net, and to compare the bone images enhanced by the QCBCT-NET with those by Cycle-GAN and U-Net. We used two phantoms of human skulls encased in acrylic, one for the training and validation datasets, and the other for the test dataset. We proposed the QCBCT-NET consisting of Cycle-GAN with residual blocks and a multi-channel U-Net using paired training data of quantitative CT (QCT) and CBCT images. The BMD images produced by QCBCT-NET significantly outperformed the images produced by the Cycle-GAN or the U-Net in mean absolute difference (MAD), peak signal to noise ratio (PSNR), normalized cross-correlation (NCC), structural similarity (SSIM), and linearity when compared to the original QCT image. The QCBCT-NET improved the contrast of the bone images by reflecting the original BMD distribution of the QCT image locally using the Cycle-GAN, and also spatial uniformity of the bone images by globally suppressing image artifacts and noise using the two-channel U-Net. The QCBCT-NET substantially enhanced the linearity, uniformity, and contrast as well as the anatomical and quantitative accuracy of the bone images, and demonstrated more accuracy than the Cycle-GAN and the U-Net for quantitatively measuring BMD in CBCT.

Recent Advances in Cone-beam CT in Oral Medicine

BACKGROUND

The cone-beam computed tomography (CBCT) technology has continuously evolved since its appearance in oral medicine in the early 2000s.

OBJECTIVES

To present recent advances in CBCT in oral medicine: i) selection of recent and consensual evidence-based sources, ii) structured summary of the information based on an iterative framework and iii) compliance with ethical, public health and patient-centered concerns.

MAIN FINDINGS

We will focus on technological advances, such as sensors and reconstruction algorithms used to improve the constant quality of the image and dosimetry. CBCT examination is now performed in almost all disciplines of oral medicine: currently, the main clinical disciplines that use CBCT acquisitions are endodontics and oral surgery, with clearly defined indications. Periodontology and ear, nose and throat medicine are more recent fields of application. For a given application and indication, the smallest possible field of view must be used. One of the major challenges in contemporary healthcare is ensuring that technological developments do not take precedence over admitted standards of care. The entire volume should be reviewed in full, with a systematic approach. All findings are noted in the patient's record and explained to the patient, including incidental findings. This presupposes the person reviewing the images is sufficiently trained to interpret such images, inform the patient and organize the clinical pathway, with referrals to other medical or oral medicine specialties as needed.

CONCLUSION

A close collaboration between dentists, medical physicists, radiologists, radiographers and engineers is critical for all aspects of CBCT technology.

The lingual foramen as a potential anatomical complication for dental implant placement in the anterior mandible

The purpose of this article is to assess the incidence of lingual foramina and associated vasculature in the anterior mandible to improve the understanding of their locations for dental implant placement. Intra-operative bleeding can be a significant complication. Pre-surgical assessment of the surgical site should be performed to identify anatomical landmarks. This can prevent a potential life-threatening haemorrhage that may compromise the airway.  Nutrient canals can occur in the anterior mandible and have been reported to cause significant bleeding if violated. Using cone beam computerized tomography (CBCT) this study defines the anatomical locales in the mandible. CBCTs of 70 patients were obtained and examined for the presence of lingual foramina. The distance of lingual foramina to the inferior border of the mandible, bifurcations and propensity for the midline were assessed. Lingual foramina were found in all of the examined mandibles with variable configurations. CBCT may be important in planning for surgical procedures in the anterior mandible to prevent an unexpected hemorrhage. The present study is limited by its sample size, method of assessment and confinement to a geographical population. The results will need validation in further studies which may incorporate multiple assessment techniques and a larger sample size with to include greater geographical distribution. Future work may seek to describe emanations of the terminus of the sublingual artery.

Morphometric Analysis of the Mandibular Canal, Anterior Loop, and Mental Foramen: A Cone-Beam Computed Tomography Evaluation

This study investigated the cone-beam computed tomography (CBCT)-based features of the mandibular canal, mental foramen, anterior loop, and accessory mental foramina with respect to age and sex. A total of 306 CBCT mandibular images were included in this retrospective study to measure the mandibular canal location and extension, the mental foramen position, the presence of the anterior loop, and the accessory mental foramina. The measurements were obtained in sagittal, coronal, and axial views. Descriptive statistics are presented. Sex-related differences, correlations, and comparisons were calculated using SPSS at 5% significance level. The mandibular canal was located more coronal and medial in male patients. The majority of cases had the mental foramen located just apical to the mandibular second premolar with a mean height of 2.94 mm and a mean length of 3.28 mm. Age affected the size of the mental foramen. The mental canal in all cases tended to show a coronal direction. Mesial extension of the anterior loop was found in 66.01% of the images while accessory mental foramina were detected in 2.6%. The complexity of the mandibular canal, mental foramen, anterior loop, and accessory mental foramina among Sudanese patients with respect to age and sex was confirmed.

Massive Radicular Cyst in the Maxillary Sinus as a Result of Deciduous Molar Tooth Pulp Necrosis

The article describes a rare case of radicular cyst associated with deciduous right upper molars in a 5 y.o. male patient. The cyst developed asymptomatically in the maxillary sinus, thus causing considerable displacement of both premolar germs. Due to the severity of surgery, the patient was treated under general anesthesia in a one-day surgery system with use of the enucleation method. The extent of the lesion results in necessity of removal of the second premolar germ. Early diagnosis of the lesion would have resulted in a more conservative treatment plan. The purpose of this article is to lay emphasis on the pedodontist's role in early diagnosis of such lesions.

The Use of Cone Beam CT(CBCT) in Differentiation of True from Mimicking Eagle's Syndrome

Differentiation of true from mimicking Eagle’s syndrome based on conventional radiography is difficult; however, cone beam computed tomography (CBCT) images can contribute to proper diagnosis of mimicking Eagle’s syndrome. The aim was to study radiological images of a 37-year old female patient (patient #1), with chronic cervicofacial pain who underwent radiological diagnosis with a conventional panoramic machine; another 75-year old male patient (patient #2), with chronic oropharyngeal pain, underwent a radiological diagnosis with the CBCT machine, with a field of a view of 16 × 12 cm. Exposure factors were 120 kVp, 7 mA, with a 20 s exposure time of acquisition. The results show a panoramic image (patient #1) with a pathologically elongated styloid process 46 mm of length, which was surgically removed, releasing the patient from further pain episodes. CBCT acquisition (patient #2) showed an impacted left maxillary canine in the edentulous maxilla and a peculiar elongation of both stylohyoid complexes as impressive, “collar-like”, bilateral, elongated, multiple segmented, calcified stylohyoid complexes, without pressure on the vital neurovascular neck structures, mimicking true Eagle’s syndrome. The impacted maxillary canine was surgically extracted with a subsequent resolution of pain episodes and the cessation of neurological complaints. The conclusions suggest that the use of CBCT images can contribute to differentiating mimicking from true Eagle’s syndrome, which has been rarely reported in the literature.

Evaluation of Midpalatal Suture Ossification Using Cone-Beam Computed Tomography: A Digital Radiographic Study

BACKGROUND

Cone beam computed tomography (CBCT) imaging techniques are the recent rage in the field of oral diagnostic imaging modality. It is noninvasive, faster and lacks anatomic superimposition. Earlier maxillary occlusal radiographs were used to assess and evaluate the mid palatal suture, but being a two dimensional imaging modality it could not assess the ossification process which takes place in multiple planes mostly due to curved nature of the palate. In this study we assessed the mid palatal suture morphology and classify them according to the variants using CBCT images.

MATERIALS AND METHODS

A total of 200 CBCT scans (95 males and 105 females) were evaluated in the present study from the archives of an imaging center. As per Angelieri classification the midpalatal suture was classified into five categories (A-E) depending on the degree of ossification that had taken place. Statistical analysis was done by Chi Square test using SPSS version 23.0.

RESULTS

There is statistically significant difference present in the stages of maturity of mid palatal suture in various age groups with Stage B is most common in Group 1 (50%), Stage C most common in Group 2 (60%) and Group 3 (40%) and Stage E more common in Group 4 (50%).

CONCLUSION

The results of the present study showed a wide variation in the initiation time and the degree of ossification and morphology of the midpalatal suture in different age groups. Although there was an increase in the closure of the suture with aging, age is not a reliable criterion for determining the open or closed nature of the suture. This finding is important in providing an idea as to how diverse is the ossification of maxillary sutures.

Cone-Beam Computed Tomography in Orthodontics

Unlike patients receiving implants or endodontic treatment, most orthodontic patients are children who are particularly sensitive to ionizing radiation. Cone-beam computed tomography (CBCT) carries risks and benefits in orthodontics. The principal risks and limitations include ionizing radiation, the presence of artifacts, higher cost, limited accessibility, and the need for additional training. However, this imaging modality has several recognized indications in orthodontics, such as the assessment of impacted and ectopic teeth, assessment of pharyngeal airway, assessment of mini-implant sites, evaluation of craniofacial abnormalities, evaluation of sinus anatomy or pathology, evaluation of root resorption, evaluation of the cortical bone plate, and orthognathic surgery planning and evaluation. CBCT is particularly justified when it brings a benefit to the patient or changes the outcome of the treatment when compared with conventional imaging techniques. Therefore, CBCT should be considered for clinical orthodontics for selected patients. Prescription of CBCT requires judicious and sound clinical judgment. The central question of this narrative review article is: when does CBCT add value to the practice of orthodontics? To answer this question, this article presents discussion on radiation dosage of CBCT and other imaging techniques used in orthodontics, limitations of CBCT in orthodontics, justifying the use of CBCT in orthodontics, and the benefits and evidence-based indications of CBCT in orthodontics. This review summarizes the central themes and topics in the literature regarding CBCT in orthodontics and presents ten orthodontic cases in which CBCT proved to be valuable.

A Novel Technique for Minimizing the Metal Artifacts on Anterior Teeth in Cone-Beam Computed Tomography

In endodontics, metal artifacts are the major weak points of employing cone-beam computed tomography (CBCT) in teeth with metallic posts and restorations. The aim of this article is to introduce a novel technique for preparation of beneficial CBCT images of anterior teeth with metal posts and/or crowns. In this novel technique, the patient who was instructed to take CBCT images for reason of implant surgery, was asked to puff out his cheeks/lips during the rotation of x-ray tube. By comparing the image taken with this technique to normal image it was obvious that after applying the cheek puffing technique, the metal artifacts around restored anterior teeth significantly reduced. Then it seems that this novel technique removes the metal artifacts from the crowned teeth to distances far from the tooth structure and enables more accurate diagnosis.

Advanced dental implant placement techniques

The availability of in office Cone Beam CT (CBCT) scanners, dental implant planning software, CAD CAM milling, and rapid printing technologies allow for the precise placement of dental implants and immediate prosthetic temporization. These technologies allow for flapless implant placement, or open flap bone reduction for "All on 4" techniques with improved preoperative planning and intraoperative performance. CBCT permits practitioners in an office setting with powerful diagnostic capabilities for the evaluation of bone quality and quantity, as well as dental and osseous pathology essential for better informed dental implant treatment. CBCT provides the convenience of in office imaging and decreased radiation exposure. Rapid printing technologies provide decreased time and high accuracy for bone model and surgical guide fabrication.

Role of Cone Beam Computed Tomography in Evaluation of Radicular Cyst mimicking Dentigerous Cyst in a 7-year-old Child: A Case Report and Literature Review

Aim

To report a rare case of large radicular cyst-associated deciduous tooth and to discuss the importance of cone beam computed tomography (CBCT) in diagnosing the condition.

Background

Radicular cyst is the most common cyst affecting the permanent teeth, but its occurrence in deciduous teeth is rare. Most of the radicular cysts are asymptomatic and are discovered accidentally when radiographs are taken. Conventional radiographs show two-dimensional images of three-dimensional objects. Cone beam computed tomography provides undistorted three-dimensional information of hard tissues and gives adequate spatial resolution.

Case report

A 7-year-old child, with a complaint of swelling in the maxillary anterior region, was diagnosed with radicular cyst in relation to primary maxillary right central incisor based on CBCT and histopathological features.

Conclusion and clinical significance

Early diagnosis and prompt treatment of radicular cyst in primary dentition is important to prevent damage to permanent tooth.

How to cite this article

Mahesh BS, Shastry SP, Murthy PS, Jyotsna TR. Role of Cone Beam Computed Tomography in Evaluation of Radicular Cyst mimicking Dentigerous Cyst in a 7-year-old Child: A Case Report and Literature Review. Int J Clin Pediatr Dent 2017;10(2):213-216.

Accuracy of linear measurements around dental implants by means of cone beam computed tomography with different exposure parameters

OBJECTIVES

The aim of this study was to determine the accuracy of linear measurements around dental implants when using CBCT unit devices presenting different exposure parameters.

METHODS

Dental implants (n = 18) were installed in the maxilla of human dry skulls, and images were obtained using two CBCT devices: G1-Care Stream 9300 (70 kVp, 6.3 mA, voxel size 0.18 mm, field of view 8 × 8 cm; Carestream Health, Rochester, NY) and G2-R100 Veraview^® (75 kVp, 7.0 mA, voxel size 0.125 mm, field of view 8 × 8 cm; J Morita, Irvine, CA). Measurements of bone thickness were performed at three points located (A) in the most apical portion of the implant, (B) 5 mm above the apical point and (C) in the implant platform. Afterwards, values were compared with real measurements obtained by an optical microscopy [control group (CG)]. Data were statistically analyzed with the significance level of p ≤ 0.05.

RESULTS

There was no statistical difference for the mean values of bone thickness on Point A (CG: 4.85 ± 2.25 mm, G1: 4.19 ± 1.68 mm, G2: 4.15 ± 1.75 mm), Point B (CG: 1.50 ± 0.84 mm, G1: 1.61 ± 1.27 mm; G2: 1.68 ± 0.82 mm) and Point C (CG: 1.78 ± 1.33 mm, G1: 1.80 ± 1.09 mm; G2: 1.64 ± 1.11 mm). G1 and G2 differed in bone thickness by approximately 0.76 mm for Point A, 0.36 mm for Point B and 0.08 mm for Point C. A lower intraclass variability was identified for CG (Point A = 0.20 ± 0.25; Point B = 0.15 ± 0.20; Point C = 0.06 ± 0.05 mm) in comparison with G1 (Point A = 0.56 ± 0.52; Point B = 0.48 ± 0.50; Point C = 0.47 ± 0.56 mm) and G2 (Point A = 0.57 ± 0.51; Point B = 0.46 ± 0.46; Point C = 0.36 ± 0.31 mm).

CONCLUSIONS

CBCT devices showed acceptable accuracy for linear measurements around dental implants, despite the exposure parameters used.

Comparison of Cone Beam Computed Tomography-Derived Alveolar Bone Density Between Subjects with and without Aggressive Periodontitis

INTRODUCTION

Understanding the changes in bone density of patients affected by aggressive periodontitis could be useful in early disease detection and proper treatment planning.

AIM

The aim of this study was to compare alveolar bone density in patients affected with aggressive periodontitis and periodontally healthy individuals using Cone Beam Computed Tomography (CBCT).

MATERIALS AND METHODS

This cross-sectional study was conducted on 20 patients with a confirmed diagnosis of aggressive periodontitis. Twenty periodontally healthy patients attending the dental clinics for implant placement or extraction of impacted third molars served as controls. Alveolar bone density was measured using CBCT scanning. Comparisons between aggressive periodontitis group and controls for age and alveolar bone density of the anterior and posterior regions were performed using an independent sample t-test. Multivariable linear regression models were also performed.

RESULTS

The differences between groups in regard to age, anterior and posterior alveolar bone density was not statistically significant (p<0.05). In the posterior region, the multivariable regression model showed that bone density was not associated with age, gender or the study groups. Whereas, in the anterior region, patient's age was found to be significantly associated with bone density, p=0.014.

CONCLUSION

Alveolar bone density as measured by CBCT in aggressive periodontitis patients was not different from periodontally healthy individuals. Further studies are needed to confirm these findings.