Decision-making patterns among expert and novice orthodontists and oral maxillofacial surgeons in the management of adults with Class III malocclusions and moderate degree of skeletal discrepancies.

OBJECTIVE: To explore the decision-making patterns among expert and novice orthodontists and oral maxillofacial surgeons in the management of adults with Class III malocclusions and moderate skeletal discrepancies. DESIGN: Self-administered questionnaire survey. SETTING: Faculty of Dentistry, National University of Singapore and the University Dental Cluster, National University Hospital, Singapore. PARTICIPANTS: A total of 55 clinicians, comprising 13 expert orthodontists, 20 novice orthodontists, 10 expert oral maxillofacial surgeons and 12 novice oral maxillofacial surgeons. METHODS: Clinicians assessed six adults with a Class III malocclusion and moderate skeletal discrepancy. They were asked to decide who could be managed exclusively by orthodontic camouflage, who would require combined orthodontic-orthognathic surgery as the only viable treatment, or who could be offered both treatment options. RESULTS: The study found variable decision-making patterns among the clinicians in each case. Only 18.2%-40.0% of clinicians agreed that the cases selected were of moderate skeletal discrepancies and could be offered both treatment options whereas the rest were either more inclined to recommend orthodontic camouflage or orthognathic surgery. Intra-clinician agreement (n = 20) was only fair (Kappa value = 0.31). There was only slight inter-clinician agreement (n = 55) on their clinical decisions (Kappa value = 0.10). Clinical experience and dental specialty did not significantly influence clinicians' decisions. Oral and maxillofacial surgeons were 1.98 times more likely to indicate orthognathic surgery as the only viable treatment compared to the orthodontists (95% confidence interval = 1.15-3.42). CONCLUSION: Variability in the patterns of decision-making for adults with a Class III malocclusion and moderate skeletal discrepancy was observed among the clinicians with low repeatability and agreement.

Comparing the effectiveness of two diagnostic approaches for the interpretation of oral radiographic lesions by dental students.

Interpreting radiographic lesions on dental radiographs is a challenging process especially for novice learners, and there is a lack of tools available to support this diagnostic process. This study introduced dental students to two diagnostic aids with contrasting reasoning approaches-ORAD DDx, which uses an analytic, forward reasoning approach, and a Radiographic Atlas, which emphasizes a non-analytic, backward reasoning approach. We compared the effectiveness of ORAD DDx and the Atlas in improving students' diagnostic accuracy and their ability to recall features of radiographic lesions. Participants (99 third-year dental students) were assigned to ORAD DDx, Atlas and Control groups. In the pre-test and post-test, participants provided their diagnosis for eight types of radiographic lesions. All groups also completed a Cued Recall Test. Feedback about ORAD DDx and the Atlas was collected. Results indicated that the Atlas was more effective than ORAD DDx in improving diagnostic accuracy (Estimated marginal mean difference = 1.88 (95% CI 0.30-3.46), p = 0.014, Cohen's d = 0.714). Participants in the Atlas group also outperformed the Control group in the recall of the lesions' radiographic features (Estimated marginal mean difference = 3.42 (95% CI 0.85-5.99), p = 0.005, Cohen's d = 0.793). Students reported that both ORAD DDx and Atlas increased their confidence and decreased the mental effort required to develop differential diagnosis (p ≤ 0.001). This study demonstrates the effectiveness of a non-analytic approach in interpreting dental radiographs among novice learners through the novel use of diagnostic aids.

Evaluation of the accuracy of cone beam computed tomography (CBCT) generated tooth replicas with application in autotransplantation.

OBJECTIVES: The primary aim of this study was to assess the linear and geometric accuracy of 3-dimensional (3D) printed tooth replicas when compared to the actual tooth. The secondary aims were to compare the accuracy of three different 3D printers and to evaluate dimensional changes of tooth replicas after sterilization. METHODS: A sample of 16 teeth were selected from recruited patients. Segmentation was carried out to generate files from the patient's cone beam computed tomography (CBCT) data, Tooth replicas were then printed using three printers making use of Polyjet, laser stereolithography (SLA) and digital light processing (DLP) technology respectively. These replicas, along with the actual tooth, were scanned by an optical scanner. Replicas were sent for sterilization and scanned again. Paired superimposition of the scans was performed. RESULTS: A mean length difference of 0.36 mm and mean geometric (root mean square [RMS]) difference of 0.56 mm was found. Qualitative analysis showed that the replicas were generally larger in size. Repeated ANOVA tests showed that the Polyjet printer had the highest accuracy (p<0.0001). After sterilization, there was an overall mean length difference of 0.10 mm and RMS deviation of 0.02 mm. CONCLUSION: This study found that there was a significant difference in linear and geometric measurements of the tooth replicas when compared to the actual tooth. This study also provides evidence that the printer which made use of Polyjet technology was able to produce more accurate models than SLA or DLP printers. Printed tooth models demonstrated clinically insignificant changes after heat sterilization. CLINICAL SIGNIFICANCE: 3D printed tooth replicas derived from the patient's CBCT data may be used in autotransplantation to increase predictability of the procedure. In order for this novel digital approach to realize its potential, it is critical to address questions as to how accurate the various printing technologies are in fabricating replicas.

Corrigendum to "A Study of Success Rate of Miniscrew Implants as Temporary Anchorage Devices in Singapore".

[This corrects the article DOI: 10.1155/2015/294670.].

Automatic segmentation of the nasal cavity and paranasal sinuses from cone-beam CT images.

PURPOSE: A patient-specific upper airway model is important for clinical, education, and research applications. Cone-beam computed tomography (CBCT) is used for imaging the upper airway but automatic segmentation is limited by noise and the complex anatomy. A multi-step level set segmentation scheme was developed for CBCT volumetric head scans to create a 3D model of the nasal cavity and paranasal sinuses. METHODS: Gaussian mixture model thresholding and morphological operators are first employed to automatically locate the region of interest and to initialize the active contour. Second, the active contour driven by the Kullback-Leibler (K-L) divergence energy in a level set framework to segment the upper airway. The K-L divergence asymmetry is used to directly minimize the K-L divergence energy on the probability density function of the image intensity. Finally, to refine the segmentation result, an anisotropic localized active contour is employed which defines the local area based on shape prior information. The method was tested on ten CBCT data sets. The results were evaluated by the Dice coefficient, the volumetric overlap error (VOE), precision, recall, and F-score and compared with expert manual segmentation and existing methods. RESULTS: The nasal cavity and paranasal sinuses were segmented in CBCT images with a median accuracy of 95.72 % [93.82-96.72 interquartile range] by Dice, 8.73 % [6.79-12.20] by VOE, 94.69 % [93.80-94.97] by precision, 97.73 % [92.70-98.79] by recall, and 95.72 % [93.82-96.69] by F-score. CONCLUSION: Automated CBCT segmentation of the airway and paranasal sinuses was highly accurate in a test sample of clinical scans. The method may be useful in a variety of clinical, education, and research applications.

A level-set based approach for anterior teeth segmentation in cone beam computed tomography images.

Cone beam CT (CBCT) has gained popularity in dentistry for 3D imaging of the jaw bones and teeth due to its high resolution and relatively lower radiation exposure compared to multi-slice CT (MSCT). However, image segmentation of the tooth from CBCT is more complex than from MSCT due to lower bone signal-to-noise. This paper describes a level-set method to extract tooth shape from CBCT images of the head. We improve the variational level set framework with three novel energy terms: (1) dual intensity distribution models to represent the two regions inside and outside the tooth; (2) a robust shape prior to impose a shape constraint on the contour evolution; and (3) using the thickness of the tooth dentine wall as a constraint to avoid leakage and shrinkage problems in the segmentation process. The proposed method was compared with several existing methods and was shown to give improved segmentation accuracy.

A two-stage rule-constrained seedless region growing approach for mandibular body segmentation in MRI.

PURPOSE: Extraction of the mandible from 3D volumetric images is frequently required for surgical planning and evaluation. Image segmentation from MRI is more complex than CT due to lower bony signal-to-noise. An automated method to extract the human mandible body shape from magnetic resonance (MR) images of the head was developed and tested. METHODS: Anonymous MR images data sets of the head from 12 subjects were subjected to a two-stage rule-constrained region growing approach to derive the shape of the body of the human mandible. An initial thresholding technique was applied followed by a 3D seedless region growing algorithm to detect a large portion of the trabecular bone (TB) regions of the mandible. This stage is followed with a rule-constrained 2D segmentation of each MR axial slice to merge the remaining portions of the TB regions with lower intensity levels. The two-stage approach was replicated to detect the cortical bone (CB) regions of the mandibular body. The TB and CB regions detected from the preceding steps were merged and subjected to a series of morphological processes for completion of the mandibular body region definition. Comparisons of the accuracy of segmentation between the two-stage approach, conventional region growing method, 3D level set method, and manual segmentation were made with Jaccard index, Dice index, and mean surface distance (MSD). RESULTS: The mean accuracy of the proposed method is [Formula: see text] for Jaccard index, [Formula: see text] for Dice index, and [Formula: see text] mm for MSD. The mean accuracy of CRG is [Formula: see text] for Jaccard index, [Formula: see text] for Dice index, and [Formula: see text] mm for MSD. The mean accuracy of the 3D level set method is [Formula: see text] for Jaccard index, [Formula: see text] for Dice index, and [Formula: see text] mm for MSD. The proposed method shows improvement in accuracy over CRG and 3D level set. CONCLUSION: Accurate segmentation of the body of the human mandible from MR images is achieved with the proposed two-stage rule-constrained seedless region growing approach. The accuracy achieved with the two-stage approach is higher than CRG and 3D level set.

An analytical drilling force model and GPU-accelerated haptics-based simulation framework of the pilot drilling procedure for micro-implants surgery training.

The placement of micro-implants is a common but relatively new surgical procedure in clinical dentistry. This paper presents a haptics-based simulation framework for the pilot drilling of micro-implants surgery to train orthodontists to successfully perform this essential procedure by tactile sensation, without damaging tooth roots. A voxel-based approach was employed to model the inhomogeneous oral tissues. A preprocessing pipeline was designed to reduce imaging noise, smooth segmentation results and construct an anatomically correct oral model from patient-specific data. In order to provide a physically based haptic feedback, an analytical drilling force model based on metal cutting principles was developed and adapted for the voxel-based approach. To improve the real-time response, the parallel computing power of Graphics Processing Units is exploited through extra efforts for data structure design, algorithms parallelization, and graphic memory utilization. A prototype system has been developed based on the proposed framework. Preliminary results show that, by using this framework, proper drilling force can be rendered at different tissue layers with reduced cycle time, while the visual display has also been enhanced.

Automatic area classification in peripheral blood smears.

Cell enumeration and diagnosis using peripheral blood smears are routine tasks in many biological and pathological examinations. Not every area in the smear is appropriate for such tasks due to severe cell clumping or sparsity. Manual working-area selection is slow, subjective, inconsistent, and statistically biased. Automatic working-area classification can reproducibly identify appropriate working smear areas. However, very little research has been reported in the literature. With the aim of providing a preprocessing step for further detailed cell enumeration and diagnosis for high-throughput screening (HTS), we propose an integrated algorithm for area classification and quantify both cell spreading and cell clumping in terms of individual clumps and the occurrence probabilities of the group of clumps over the image. Comprehensive comparisons are presented to compare the effect of these quantifications and their combinations. Our experiments using images of Giemsa-stained blood smears show that the method is efficient, accurate (above 88.9% hit rates for all areas in the validation set of 140 images), and robust (above 78.1% hit rates for a test set of 4878 images). This lays a good foundation for fast working-area selection in HTS.