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Orthodontists, surgeons, and patients have taken an interest in using clear aligners in combination with orthognathic surgery. This study aimed to evaluate the accuracy of tooth movements with clear aligners during presurgical orthodontics using novel 3-dimensional superimposition techniques.The study sample consisted of 20 patients who have completed presurgical orthodontics using Invisalign clear aligners. Initial (pretreatment) digital dental models, presurgical digital dental models, and ClinCheck prediction models were obtained. Presurgical models were superimposed onto initial ones using stable anatomic landmarks; ClinCheck models were superimposed onto presurgical models using surface best-fit superimposition. Five hundred forty-five teeth were measured for 3 angular movements (buccolingual torque, mesiodistal tip, and rotation) and 4 linear movements (buccolingual, mesiodistal, vertical, and total scalar displacement). The predicted tooth movement was compared with the achieved amount for each movement and tooth, using both percentage accuracy and numerical difference.Average percentage accuracy (63.4% ± 11.5%) was higher than in previously reported literature. The most accurate tooth movements were buccal torque and mesial displacement compared with lingual torque and distal displacement, particularly for mandibular posterior teeth. Clinically significant inaccuracies were found for the buccal displacement of maxillary second molars, lingual displacement of all molars, intrusion of mandibular second molars, the distal tip of molars, second premolars, and mandibular first premolars, buccal torque of maxillary central and lateral incisors, and lingual torque of premolars and molars.Superimposition techniques used in this study lay the groundwork for future studies to analyze advanced clear aligner patients. Invisalign is a treatment modality that can be considered for presurgical orthodontics-tooth movements involved in arch leveling and decompensation are highly accurate when comparing the simulated and the clinically achieved movements.

This study was performed to evaluate the condylar displacement and associated condylar remodeling in class III patients following mandibular setback surgery via sagittal split ramus osteotomy (SSRO). The sample comprised of 26 condyles of 13 subjects (mean age of 21.2±2.6 y). We evaluated patients with mandibular prognathism and facial asymmetry who had undergone SSRO for mandibular setback at Korea University Hospital between January 2016 and December 2018. Three-dimensional segmentation of the mandibular condyles was done using the initial cone-beam computed tomography scan and scan taken 12 months postoperatively or later. Quantitative assessments of the 3-dimensional condylar displacement from T0 to T1 and bony remodeling of 8 regions of the condylar head were performed. The correlation between the condylar displacement and condylar head remodeling on the deviated (D) and nondeviated (ND) sides was analyzed. Significant correlations between condylar displacement and surface remodeling were observed in both D and ND condyles. The anteroposterior condylar displacement was significantly different between the D and ND sides (P=0.007). There was no significant difference in condylar remodeling between the 2 sides. Condylar displacement and adaptive remodeling after SSRO varied greatly among individuals. Compared with displacement in the ND condyle, displacement in the D condyle has a greater association with condylar remodeling in both D and ND condyles. There is no significant difference in condylar head remodeling between D and ND condyles.

The aim was to have a three-dimensional evaluation of radiographic changes after indirect pulp capping (IPC) with silver diamine fluoride (SDF) with or without potassium iodide (KI) and resin modified glass ionomer cement (RMGIC) in deep carious young permanent molars using cone beam computed tomography (CBCT). 108 first permanent molars with deep occlusal cavitated caries lesions, in forty-nine 6–9-year-old children, were randomly allocated to one of 3 groups (n=36) and treated with SDF+KI, SDF and RMGIC as IPC materials. CBCT scans were taken at 0 and at 12-months to assess tertiary dentin formation (volume and grey level intensity), increase in root length, and pathological changes as secondary caries, periapical radiolucency, internal resorption and obliteration of the pulp. The three-dimensional image analysis procedures were performed using ITK-SNAP and 3D Slicer CMF. Comparisons were made using analysis of variance with a fixed effect for treatment and random effects for patient, and patient-by-treatment to account for within-patient correlations. A two-sided 5% significance level was used. There were no significant differences among the three groups regarding tertiary dentin volume (p=0.712) and grey level intensity (p=0.660), increase in root length (p=0.365), prevention of secondary caries (p=0.63) and periapical radiolucency (p=0.80) in the analysed 69 CBCT scans. The study did not find differences among the groups regarding quality and quantity of tertiary dentin formed, increase in root length, absence of secondary caries and other signs of failure as shown by CBCT. Clinical Significance: The results show no significant differences in radiographic outcomes (quality and quantity of tertiary dentin formed, increase in root length, absence of secondary caries and other signs of failure) when using SDF+KI, SDF and RMGIC in IPC. The results of this study can help guide treatment decision making regarding use of SDF and SDF+KI as IPC materials in deep cavitated lesions.

This study was performed to evaluate condylar position and angulation after asymmetric mandibular setback between a conventional (CA) and surgery-first approach (SFA) using three-dimensional analysis. The condylar positions of 30 patients with skeletal Class III malocclusion and facial asymmetry who underwent 1-jaw (sagittal split ramus osteotomy) or 2-jaw orthognathic surgery (Le Fort I osteotomy and sagittal split ramus osteotomy) with CA (n = 18) or SFA (n = 12) from 2 university hospitals were studied. The three-dimensional assessment of condylar changes was performed using computed tomography images at the initial time point (T0) and at least 6 months after surgery (T1). Segmentation of condyles and cranial base assessment from cone-beam computed tomography images were performed using ITK-SNAP software (version 3.4.0). Condylar position and angulation changes were calculated using 3D Slicer software (version 4.10.2), and statistical analysis was performed. No significant translational or rotational condylar changes were observed between the deviated and non-deviated sides in each group or between the CA and SFA groups except yaw ( p = 0.014). Linear mixed-model analysis and multi-variate analysis showed no significant difference between the CA and SFA groups. Surgery-first approach might not be associated with more harmful effects on the condylar position and angulation changes as compared with CA.

In the digital dentistry era, new tools, algorithms, data science approaches, and computer applications are available to researchers and clinicians. However, there is also a strong need for better knowledge and understanding of multisource data applications, including three-dimensional imaging information such as cone-beam computed tomography images and digital dental models for multidisciplinary cases. In addition, artificial intelligence models and automated clinical decision systems are rising. The clinician needs to plan the treatment based on state-of-the-art diagnosis for better and more personalized treatment. This article aimed to review basic concepts and the current panorama of digital implant planning in orthodontics, with open-source and closed-source tools for assessing cone-beam computed images and digital dental models. The visualization and processing of the three-dimensional data allow better implant planning based on bone conditions, adjacent teeth and root positions, and the prognosis of the case. We showed that many tools for assessment, segmentation, and visualization of cone-beam computed tomographic images and digital dental models could facilitate the treatment planning of patients needing implants or space closure. The tools and approaches presented are toward personalized treatment and better prognosis, following the path to a more automated clinical decision system based on multisource three-dimensional data, artificial intelligence models, and digital planning. In summary, the orthodontist needs to analyze each patient individually and use different software or tools that better fit their practice, allowing efficient treatment planning and satisfactory results with an adequate prognosis.

A growing variety of intraoral appliances are being used in dental practice. Insight about the range of applications and practice workflow as well as factors effecting change in practice trends was sought.A survey was developed to ascertain intraoral appliance use, fabrication methods, staff involvement, and patient experiences in dental practice. The survey was sent to ADA Clinical Evaluators Panel members on July 25, 2022, in Qualtrics and remained open for 2 weeks. After 1 week, those who had not responded yet were sent reminders. Data were analyzed descriptively in SAS Version 9.4.Of the 286 respondents (a 28% response rate), 88% reported providing patients with intraoral appliances apart from providing patients with removable partial dentures. More than one-half of the respondents reported offering bleaching trays (88%), flippers or Essix retainers (83%), splints (81%), athletic mouthguards (72%), orthodontic retainers (60%), fluoride trays (56%), and clear aligners (55%). The biggest changes to practice in the past 5 years with respect to intraoral appliances have been intraoral scanning (56%) and the increase in the variety of intraoral appliances in use. Although 72% reported dental assistants were involved in obtaining impressions and fabricating models for intraoral appliances and 76% reported that laboratory technicians were involved in fabricating intraoral appliances, nearly all (97%) respondents indicated that the dentist was involved in the delivery of appliances to patients. Patient education about the use of their appliances appeared to be delivered using multiple approaches, given that it was reported to occur orally (95%), in print (61%), and visually (42%). That 90% of respondents reported having engaged in continuing education courses about intraoral appliances aligns with these appliances becoming a routine part of dental practice, with both increasing applications and improvements in the relevant technology.Intraoral appliances were found to be used commonly in dental practice, and often multiple members of the dental care team were engaged in the fabrication processes.There likely will be ongoing interest in relevant learning opportunities about technologic advances as well as the increasingly diverse list of applications for intraoral appliances.

Osteoarthritis of the temporomandibular joint (TMJ OA) is the most common disorder of the TMJ. A clinical decision support (CDS) system designed to detect TMJ OA could function as a useful screening tool as part of regular check-ups to detect early onset. This study implements a CDS concept model based on Random Forest and dubbed RF(+) to predict TMJ OA with the hypothesis that a model which leverages high-resolution radiological and biomarker data in training only can improve predictions compared with a baseline model which does not use privileged information. We found that the RF(+) model can outperform the baseline model even when privileged features are not of gold standard quality. Additionally, we introduce a novel method for post-hoc feature analysis, finding shortRunHighGreyLevelEmphasis of the lateral condyles and joint distance to be the most important features from the privileged modalities for predicting TMJ OA.

This paper proposes a machine learning model using privileged information (LUPI) and normalized mutual information feature selection method (NMIFS) to build a robust and accurate framework to diagnose patients with Temporomandibular Joint Osteoarthritis (TMJ OA). To build such a model, we employ clinical, quantitative imaging and additional biological markers as privileged information. We show that clinical features play a leading role in the TMJ OA diagnosis and quantitative imaging features, extracted from cone-beam computerized tomography (CBCT) scans, improve the model performance. As the proposed LUPI model employs biological data in the training phase (which boosted the model performance), this data is unnecessary for the testing stage, indicating the model can be widely used even when only clinical and imaging data are collected. The model was validated using 5-fold stratified cross-validation with hyperparameter tuning to avoid the bias of data splitting. Our method achieved an AUC, specificity and precision of 0.81, 0.79 and 0.77, respectively.

This randomized clinical trial aimed to compare the three-dimensional dentoalveolar maxillary changes after anterior open bite treatment with bonded spurs and build-ups versus bonded spurs alone. Patients from 7 to 11 years of age with anterior open bite were randomly allocated into two groups. Bonded spurs and posterior build-ups were used in the experimental group and only bonded spurs were used in the comparison group. The randomization sequence was generated at www.randomization.com. Opaque, sealed and sequentially numbered envelopes were part of the allocation concealment. Digital dental models were acquired before (T1) and after 12 months of treatment (T2) and de-identified for analysis purposes. Three-dimensional changes of maxillary permanent incisors and first molars were evaluated by means of T1 and T2 dental model superimposition. Landmark-based registration on the posterior teeth and registration on the palate using regions of interest were performed. T or Mann–Whitney U tests were used for intergroup comparisons (P Trial registration: Clinicaltrials.gov; NCT03702881, date of registration: October 11, 2018.

In order to diagnose TMJ pathologies, we developed and tested a novel algorithm, MandSeg, that combines image processing and machine learning approaches for automatically segmenting the mandibular condyles and ramus. A deep neural network based on the U-Net architecture was trained for this task, using 109 cone-beam computed tomography (CBCT) scans. The ground truth label maps were manually segmented by clinicians. The U-Net takes 2D slices extracted from the 3D volumetric images. All the 3D scans were cropped depending on their size in order to keep only the mandibular region of interest The same anatomic cropping region was used for every scan in the dataset. The scans were acquired at different centers with different resolutions. Therefore, we resized all scans to 512×512 in the pre-processing step where we also performed contrast adjustment as the original scans had low contrast. After the pre-processing, around 350 slices were extracted from each scan, and used to train the U-Net model. For the cross-validation, the dataset was divided into 10 folds. The training was performed with 60 epochs, a batch size of 8 and a learning rate of 2×10(−5). The average performance of the models on the test set presented 0.95 ± 0.05 AUC, 0.93 ± 0.06 sensitivity, 0.9998 ± 0.0001 specificity, 0.9996 ± 0.0003 accuracy, and 0.91 ± 0.03 F1 score. This study findings suggest that fast and efficient CBCT image segmentation of the mandibular condyles and ramus from different clinical data sets and centers can be analyzed effectively. Future studies can now extract radiomic and imaging features as potentially relevant objective diagnostic criteria for TMJ pathologies, such as osteoarthritis (OA). The proposed segmentation will allow large datasets to be analyzed more efficiently for disease classification.

In this paper, machine learning approaches are proposed to support dental researchers and clinicians to study the shape and position of dental crowns and roots, by implementing a Patient Specific Classification and Prediction tool that includes RootCanalSeg and DentalModelSeg algorithms and then merges the output of these tools for intraoral scanning and volumetric dental imaging. RootCanalSeg combines image processing and machine learning approaches to automatically segment the root canals of the lower and upper jaws from large datasets, providing clinical information on tooth long axis for orthodontics, endodontics, prosthodontic and restorative dentistry procedures. DentalModelSeg includes segmenting the teeth from the crown shape to provide clinical information on each individual tooth. The merging algorithm then allows users to integrate dental models for quantitative assessments. Precision in dentistry has been mainly driven by dental crown surface characteristics, but information on tooth root morphology and position is important for successful root canal preparation, pulp regeneration, planning of orthodontic movement, restorative and implant dentistry. In this paper we propose a patient specific classification and prediction of dental root canal and crown shape analysis workflow that employs image processing and machine learning methods to analyze crown surfaces, obtained by intraoral scanners, and three-dimensional volumetric images of the jaws and teeth root canals, obtained by cone beam computed tomography (CBCT).

Diagnosis of temporomandibular joint (TMJ) Osteoarthritis (OA) before serious degradation of cartilage and subchondral bone occurs can help prevent chronic pain and disability. Clinical, radiomic, and protein markers collected from TMJ OA patients have been shown to be predictive of OA onset. Since protein data can often be unavailable for clinical diagnosis, we harnessed the learning using privileged information (LUPI) paradigm to make use of protein markers only during classifier training. Three different LUPI algorithms are compared with traditional machine learning models on a dataset extracted from 92 unique OA patients and controls. The best classifier performance of 0.80 AUC and 75.6 accuracy was obtained from the KRVFL+ model using privileged protein features. Results show that LUPI-based algorithms using privileged protein data can improve final diagnostic performance of TMJ OA classifiers without needing protein microarray data during classifier diagnosis.

Objectives To evaluate root resorption of lower incisors and canines quantitatively in a group of patients who underwent orthodontic treatment with piezocision and/or a collagen reinforcement technique with a fully resorbable three-dimensional (3D) collagen xenograft matrix compared with a control group. Materials and Methods The study sample of this secondary analysis consisted of 32 periodontally healthy patients with angle Class I malocclusion or mild Class II or III malocclusion and moderate irregularity index scores who underwent orthodontic treatment and had before (T0) and after treatment (T1) cone-beam computed tomography scans. Root resorption of lower incisors and canines was assessed quantitatively in the following four groups: the control group received orthodontic treatment without piezocision, experimental group 1 received orthodontic treatment with piezocision, experimental group 2 received orthodontic treatment with piezocision and a 3D collagen matrix, and experimental group 3 received orthodontic treatment with a 3D collagen matrix. Results An overall statistically significant decrease in root length from T0 to T1 for all groups was observed (P < .05). However, there was no significant difference among the groups in the amount of root length decrease from T0 to T1. Conclusions Orthodontic treatment combined with piezocision does not increase the risk of root resorption of lower incisors and canines when compared with orthodontic treatment without acceleration techniques. More studies with larger samples should be undertaken to confirm these results.

The Data Storage for Computation and Integration (DSCI) proposes management innovations for web-based secure data storage, algorithms deployment, and task execution. Its architecture allows inclusion of plugins for upload, browsing, sharing, and task execution in remote computing grids. Here, we demonstrate the DSCI implementation and the deployment of Image processing tools (TMJSeg), machine learning algorithms (MandSeg, DentalModelSeg), and advanced statistical packages (Multivariate Functional Shape Data Analysis, MFSDA), with data transfer and task execution handled by the clusterpost plug-in. Due to its comprehensive web-based design, local software installation is no longer required. The DSCI aims to enable and maintain a distributed computing and collaboration environment across multi-site clinical centers for the data processing of multisource features such as clinical, biological markers, volumetric images, and 3D surface models, with particular emphasis on analytics for temporomandibular joint osteoarthritis (TMJ OA).

Disordered craniofacial development frequently results in definitive facial asymmetries that can significantly impact a person's social and functional well-being. The mandible plays a prominent role in defining facial symmetry and, as an active region of growth, commonly acquires asymmetric features. Additionally, syndromic mandibular asymmetry characterizes craniofacial microsomia (CFM), the second most prevalent congenital craniofacial anomaly (1:3000 to 1:5000 live births) after cleft lip and palate. We hypothesized that asymmetric rates of mandibular growth occur in the context of syndromic and acquired facial asymmetries.To test this hypothesis, a spherical harmonic-based shape correspondence algorithm was applied to quantify and characterize asymmetries in mandibular growth and remodeling in 3 groups during adolescence. Longitudinal time points were automatically registered, and regions of the condyle and posterior ramus were selected for growth quantification. The first group (n = 9) had a diagnosis of CFM, limited to Pruzansky-Kaban type I or IIA mandibular deformities. The second group (n = 10) consisted of subjects with asymmetric, nonsyndromic dentofacial asymmetry requiring surgical intervention. A control group (n = 10) of symmetric patients was selected for comparison. A linear mixed model was used for the statistical comparison of growth asymmetry between the groups.Initial mandibular shape and symmetry displayed distinct signatures in the 3 groups (P 0.001), with the greatest asymmetries in the condyle and ramus. Similarly, mandibular growth had unique patterns in the groups. The dentofacial asymmetry group was characterized by significant asymmetry in condylar and posterior ramal remodeling with growth (P 0.001). The CFM group was characterized by asymmetric growth of the posterior ramus (P 0.001) but relatively symmetric growth of the condyles (P = 0.47).Forms of CFM are characterized by active and variable growth of the dysplastic side, which has a distinct pattern from other disorders of mandibular growth.