Clinical evaluation of Artificial Intelligence Driven Remote Monitoring technology for assessment of patient oral hygiene during orthodontic treatment.

INTRODUCTION: This study aimed to clinically evaluate the accuracy of Dental Monitoring's (DM) artificial intelligence (AI) image analysis and oral hygiene notification algorithm in identifying oral hygiene and mucogingival conditions. METHODS: Twenty-four patients seeking orthodontic therapy were monitored by DM oral hygiene protocol during their orthodontic treatment. During the bonding appointment and at each of 10 subsequent adjustment visits, a total of 232 clinical oral examinations were performed to assess the presence of the 3 oral hygiene parameters that DM monitors. In each clinical timepoint, the subjects took an oral DM scan and received a notification regarding their current oral status at that moment in time. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated to evaluate AI and clinical assessment of plaque, gingivitis, and recession. RESULTS: A total of 232 clinical time points have been evaluated clinically and by the DM AI algorithm. For DM's AI detection of plaque and calculus, gingivitis, and recession, the sensitivity was 0.53, 0.35, and 0.22; the specificity was 0.94, 0.96, and 0.99; and the accuracy was 0.60, 0.49, and 0.72, respectively. CONCLUSIONS: DM's oral hygiene notification algorithm has low sensitivity, high specificity, and moderate accuracy. This indicates a tendency of DM to underreport the presence of plaque, gingivitis, and recession.

Connecting the dots towards precision orthodontics.

Precision orthodontics entails the use of personalized clinical, biological, social and environmental knowledge of each patient for deep individualized clinical phenotyping and diagnosis combined with the delivery of care using advanced customized devices, technologies and biologics. From its historical origins as a mechanotherapy and materials driven profession, the most recent advances in orthodontics in the past three decades have been propelled by technological innovations including volumetric and surface 3D imaging and printing, advances in software that facilitate the derivation of diagnostic details, enhanced personalization of treatment plans and fabrication of custom appliances. Still, the use of these diagnostic and therapeutic technologies is largely phenotype driven, focusing mainly on facial/skeletal morphology and tooth positions. Future advances in orthodontics will involve comprehensive understanding of an individual's biology through omics, a field of biology that involves large-scale rapid analyses of DNA, mRNA, proteins and other biological regulators from a cell, tissue or organism. Such understanding will define individual biological attributes that will impact diagnosis, treatment decisions, risk assessment and prognostics of therapy. Equally important are the advances in artificial intelligence (AI) and machine learning, and its applications in orthodontics. AI is already being used to perform validation of approaches for diagnostic purposes such as landmark identification, cephalometric tracings, diagnosis of pathologies and facial phenotyping from radiographs and/or photographs. Other areas for future discoveries and utilization of AI will include clinical decision support, precision orthodontics, payer decisions and risk prediction. The synergies between deep 3D phenotyping and advances in materials, omics and AI will propel the technological and omics era towards achieving the goal of delivering optimized and predictable precision orthodontics.

Evaluation of upper airway characteristics in patients with and without sleep apnea using cone-beam computed tomography and computational fluid dynamics.

OBJECTIVE: To determine if upper airway characteristics and airway pressure change significantly between low risk, healthy non-OSA subjects, and OSA subjects during respiration using cone-beam computed tomography (CBCT) imaging and steady-state k-ω model computational fluid dynamics (CFD) fluid flow simulations, respectively. MATERIALS AND METHODS: CBCT scans were collected at both end-inhalation and end-exhalation for 16 low-risk non-OSA subjects and compared to existing CBCT data from 7 OSA subjects. The CBCT images were imported into Dolphin Imaging and the upper airway was segmented into stereolithography (STL) files for area and volumetric measurements. Subject models that met pre-processing criteria underwent CFD simulations using ANSYS Fluent Meshing (Canonsburg, PA) in which unstructured mesh models were generated to solve the standard dual equation turbulence model (k-ω). Objective and supplemental descriptive measures were obtained and statistical analyses were performed with both parametric and non-parametric tests to evaluate statistical significance at P < .05. RESULTS: Regarding area and volumetric assessments, there were statistically significant mean differences in Total Volume and Minimum CSA between non-OSA and OSA groups at inhalation and exhalation (P = .002, .003, .004, and .007), respectively. There were also statistically significant mean differences in volume and min CSA between the inhalation and exhalation for the non-OSA group (P < .001 and .002), respectively. CONCLUSION: While analysis of the CFD simulation was limited by the collected data available, a finding consistent with published literature was that the OSA subject group simulation models depicted the point of lowest pressure coinciding with the area of maximum constriction.

Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health.

Machine Learning (ML), a subfield of Artificial Intelligence (AI), is being increasingly used in Orthodontics and craniofacial health for predicting clinical outcomes. Current ML/AI models are prone to accentuate racial disparities. The objective of this narrative review is to provide an overview of how AI/ML models perpetuate racial biases and how we can mitigate this situation. A narrative review of articles published in the medical literature on racial biases and the use of AI/ML models was undertaken. Current AI/ML models are built on homogenous clinical datasets that have a gross underrepresentation of historically disadvantages demographic groups, especially the ethno-racial minorities. The consequence of such AI/ML models is that they perform poorly when deployed on ethno-racial minorities thus further amplifying racial biases. Healthcare providers, policymakers, AI developers and all stakeholders should pay close attention to various steps in the pipeline of building AI/ML models and every effort must be made to establish algorithmic fairness to redress inequities.

Novel Machine Learning Algorithms for Prediction of Treatment Decisions in Adult Patients With Class III Malocclusion.

BACKGROUND: Management of Class III (Cl III) dentoskeletal phenotype is often expert-driven. PURPOSE: The aim is to identify critical morphological features in postcircumpubertal Cl III treatment and appraise the predictive ability of innovative machine learning (ML) algorithms for adult Cl III malocclusion treatment planning. STUDY DESIGN: The Orthodontics Department at the University of Illinois Chicago undertook a retrospective cross-sectional study analyzing Cl III malocclusion cases (2003-2020) through dental records and pretreatment lateral cephalograms. PREDICTOR: Forty features were identified through a literature review and gathered from pretreatment records, serving as ML model inputs. Eight ML models were trained to predict the best treatment for adult Cl III malocclusion. OUTCOME VARIABLE: Predictive accuracy, sensitivity, and specificity of the models, along with the highest-contributing features, were evaluated for performance assessment. COVARIATES: Demographic covariates, including age, gender, race, and ethnicity, were assessed. Inclusion criteria targeted patients with cervical vertebral maturation stage 4 or above. Operative covariates such as tooth extraction and types of orthognathic surgical maneuvers were also analyzed. ANALYSES: Demographic characteristics of the camouflage and surgical study groups were described statistically. Shapiro-Wilk Normality test was employed to check data distribution. Differences in means between groups were evaluated using parametric and nonparametric independent sample tests, with statistical significance set at <0.05. RESULTS: The study involved 182 participants; 65 underwent camouflage mechanotherapy, and 117 received orthognathic surgery. No statistical differences were found in demographic characteristics between the two groups (P > .05). Extreme values of pretreatment parameters suggested a surgical approach. Artificial neural network algorithms predicted treatment approach with 91% accuracy, while the Extreme Gradient Boosting model achieved 93% accuracy after recursive feature elimination optimization. The Extreme Gradient Boosting model highlighted Wit's appraisal, anterior overjet, and Mx/Md ratio as key predictors. CONCLUSIONS: The research identified significant cephalometric differences between Cl III adults requiring orthodontic camouflage or surgery. A 93% accurate artificial intelligence model was formulated based on these insights, highlighting the potential role of artificial intelligence and ML as adjunct tools in orthodontic diagnosis and treatment planning. This may assist in minimizing clinician subjectivity in borderline cases.

Robert M. Ricketts and Rudolf Slavicek: dentistry by the rules of nature.

The stomatognathic structures act as a complex and integrated system, thereby accomplishing several essential functions of the body. Aside from participating in food digestion, they are key for respiration and swallowing and play a central role in social interaction and stress management. The lifeworks of Robert M. Ricketts (1920-2003), an American orthodontist, and Rudolf Slavicek (1928-2022), an Austrian prosthodontist, were centered on this understanding. Both were educated in the time of gnathology, functional dentistry, and cephalometry and were ready to challenge conventional knowledge and traditions, leading toward innovation. As untiring clinicians, researchers, and mentors, they were fully invested in the study of the stomatognathic system, considering its morphology, dynamics, growth patterns, evolution, and interactions with the body and mind. Based on their extensive knowledge of the masticatory system, they advanced dentistry both with theoretical notions and by implementing new diagnostic and therapeutic concepts, thus reinforcing the idea of dentistry as a medical discipline requiring interdisciplinary effort. Their heritage is represented by numerous publications, discoveries, and inventions that inspire the dental community to follow their exemplary approach to the individualized care of patients. Their knowledge and passion are further passed on through their students. As part of their legacy, they prepared the ground for new research aimed at fostering advancements in occlusion medicine, hence supporting education in oral health.

The use of teleorthodontics during the COVID-19 pandemic and beyond - perspectives of patients and providers.

BACKGROUND: The COVID-19 pandemic significantly impacted dental services, resulting in reduced staff availability, limited appointments, and some dental clinics even being forced to close their doors. Despite these challenges, the need for dental consultants remained present, particularly in emergency situations. One area of orthodontics that had seen a surge in demand during the pandemic is Teleorthodontics. With the help of Teleorthodontics, orthodontic consultations, assessments, and even treatment monitoring could be conducted remotely, making it a safe and convenient option for patients during those challenging times. AIM: This survey aimed to evaluate the acceptance of patients and their orthodontists on the use of different modes of communication through Teleorthodontics during the COVID-19 pandemic and their willingness to continue using this in the future. METHODS: An online survey instrument in Qualtrics was distributed to orthodontic patients at the University of Illinois, Chicago. The survey was available on a rolling basis for up to 6 months. A total number of 364 partients voluntarily participated in the survey. The Faculty and Residents were also asked to participate in a survey through recruitment via their UIC email addresses. RESULTS: According to our survey, both patients and providers showed acceptance of Teleorthodontics and have used it in different forms during orthodontic treatment. The application is easy-to-use, convenient, and not at all time-consuming. Overall satisfaction with using this application was recorded at 92%, with 66% of patients stating that it saved them time by eliminating the need to travel to the orthodontic clinic. 30% of providers found that the interaction with patients using Teleorthodontics was a positive experience and would recommend it in future. CONCLUSION: Teleorthodontics has shown great potential, particularly in follow-up cases, and holds promise as a valuable tool for online remote dental consultations in the future.

In-vivo evaluation of Artificial Intelligence Driven Remote Monitoring technology for tracking tooth movement and reconstruction of 3-dimensional digital models during orthodontic treatment.

INTRODUCTION: An in-vivo evaluation of the Dental Monitoring (DM; Paris, France) Artificial Intelligence Driven Remote Monitoring technology was conducted in an active clinical setting. Our objectives were to compare the accuracy and validity of the 3-dimensional (3D) digital models remotely generated from the DM application to 3D Digital Models generated from the iTero Element 5D intraoral scanner (Align Technologies, San Jose, Calif) of patients' dentition during in-vivo fixed orthodontic treatment. METHODS: The orthodontic treatment of 24 patients (aged 14-55 years) was tracked across an average of 13.4 months. Scans of the maxillary and mandibular arches of each patient were taken by an iTero intraoral scanner and with the DM application before treatment initiation without (T0) and with (T1) the fixed orthodontic appliances and at every in-person adjustment appointment (T2-T10). The global deviation between the reconstructed digital models from the DM and iTero scans was compared at each time point using Geomagic Control-X 2020 (3D Systems, Rock Hill, SC). Descriptive analysis was conducted to determine the mean deviation at each time point for the maxillary and mandibular arches, to compare the maxilla and mandible mean deviations at each time point to the null hypothesis mean of 0 mm and the paired mean of the average at each time point between the maxilla and mandible. RESULTS: The findings revealed no clinically significant difference between the reconstructed digital models generated by the iTero IOS and the remotely reconstructed digital dental models generated by the DM application. CONCLUSION: DM artificial intelligence tracking algorithm can track tooth movement and reconstruct 3D digital models to a clinically acceptable degree for orthodontic application.

Effectiveness of AI-driven remote monitoring technology in improving oral hygiene during orthodontic treatment.

OBJECTIVE: This study aimed to evaluate the effectiveness of Dental Monitoring™ (DM™) Artificial Intelligence Driven Remote Monitoring Technology (AIDRM) technology in improving the patient's oral hygiene during orthodontic treatment through AI-based personalized active notifications. METHODS: A prospective clinical study was conducted on two groups of orthodontic patients. DM Group: (n = 24) monitored by DM weekly scans and received personalized notifications on the DM smartphone application regarding their oral hygiene status. Control Group (n = 25) not monitored by DM. Both groups were clinically assessed using Plaque Index (OPI) and the Modified Gingival Index (MGI). DM Group was followed for 13 months and the Control Group was followed for 5 months. Student-independent t test and paired t tests were used to investigate the mean differences between study groups and between time points for each group respectively. RESULTS: At all time points, the mean differences indicated that the DM group had lower OPI and MGI values than the control group. The mean value for OPI and MGI were statistically significantly lower in the DM group (OPI = 1.96, MGI = 1.56) than in the control group (OPI = 2.41, MGI = 2.17) after 5 months. A rapid increase in mean OPI and MGI values was found between T0 and T1 for both study groups. A plateau effect for OPI scores appeared to occur from T1 to T5 for both study groups, but the plateau effect seemed to be more pronounced for the DM group than the study group. The MGI values for both study groups also increased dramatically from baseline to T5, however, a plateau effect was not observed. CONCLUSIONS: The oral hygiene of orthodontic patients rapidly worsens over the first 3 months and plateaus after about 5 months of treatment. AIDRM by weekly DM scans and personalized active notifications may improve oral hygiene over time in orthodontic patients.

Blockchain technology and federated machine learning for collaborative initiatives in orthodontics and craniofacial health.

There is a paucity of largescale collaborative initiatives in orthodontics and craniofacial health. Such nationally representative projects would yield findings that are generalizable. The lack of large-scale collaborative initiatives in the field of orthodontics creates a deficiency in study outcomes that can be applied to the population at large. The objective of this study is to provide a narrative review of potential applications of blockchain technology and federated machine learning to improve collaborative care. We conducted a narrative review of articles published from 2018 to 2023 to provide a high level overview of blockchain technology, federated machine learning, remote monitoring, and genomics and how they can be leveraged together to establish a patient centered model of care. To strengthen the empirical framework for clinical decision making in healthcare, we suggest use of blockchain technology and integrating it with federated machine learning. There are several challenges to adoption of these technologies in the current healthcare ecosystem. Nevertheless, this may be an ideal time to explore how best we can integrate these technologies to deliver high quality personalized care. This article provides an overview of blockchain technology and federated machine learning and how they can be leveraged to initiate collaborative projects that will have the patient at the center of care.

AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages.

OBJECTIVE: A study of supervised automated classification of the cervical vertebrae maturation (CVM) stages using deep learning (DL) network is presented. A parallel structured deep convolutional neural network (CNN) with a pre-processing layer that takes X-ray images and the age as the input is proposed. METHODS: A total of 1018 cephalometric radiographs were labelled and classified according to the CVM stages. The images were separated according to gender for better model-fitting. The images were cropped to extract the cervical vertebrae automatically using an object detector. The resulting images and the age inputs were used to train the proposed DL model: AggregateNet with a set of tunable directional edge enhancers. After the features of the images were extracted, the age input was concatenated to the output feature vector. To have the parallel network not overfit, data augmentation was used. The performance of our CNN model was compared with other DL models, ResNet20, Xception, MobileNetV2 and custom-designed CNN model with the directional filters. RESULTS: The proposed innovative model that uses a parallel structured network preceded with a pre-processing layer of edge enhancement filters achieved a validation accuracy of 82.35% in CVM stage classification on female subjects, 75.0% in CVM stage classification on male subjects, exceeding the accuracy achieved with the other DL models investigated. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If AggregateNet is used without directional filters, the test accuracy decreases to 80.0% on female subjects and to 74.03% on male subjects. CONCLUSION: AggregateNet together with the tunable directional edge filters is observed to produce higher accuracy than the other models that we investigated in the fully automated determination of the CVM stages.

Cone-beam computed tomography assessment of maxillary anterior alveolar bone remodelling in extraction and non-extraction orthodontic cases using stable extra-alveolar reference.

OBJECTIVE: To explore alveolar cortical positional change in response to tooth movement in extraction and non-extraction orthodontic cases, using cone-beam computed tomography (CBCT) and stable extra-alveolar references. MATERIALS AND METHODS: The pre-treatment (T1) and post-treatment (T2) CBCT scans of 25 extraction (EXT) and matched 25 non-extraction (Non-EXT) orthodontic cases were imported into Dolphin Imaging 3D, and oriented uniformly. Sagittal and axial CBCT cross-sections were traced using customized software-generated guides. The displacement of teeth and alveolar bone cortices were automatically measured using the palatal plane (PP) and the line perpendicular to PP and passing Sella as reference. Intra- and inter-group differences between T1 and T2 were analysed. Subjects were also superimposed three-dimensionally using Geomagic Control X for qualitative analysis of cortical remodelling. RESULTS: The EXT group showed incisor retraction, while the Non-EXT group exhibited statistically significant incisor anterior tipping (P < .05). In EXT, both the labial and palatal cortices are resorbed. Non-EXT showed labial cortex anterior modelling, and statistically significant palatal cortex resorption (P < .05). In both groups, statistically significant decrease in total and palatal alveolar widths, increase in labial widths, and palatal dehiscence were observed. Comparatively, EXT showed significantly more incisal total and palatal width decrease and palatal vertical bone loss. CONCLUSION: Labial cortical remodelling was shown to follow anterior tooth movement, but the palatal cortical response to incisor retraction and labial cortical remodelling in general remained inconclusive. Narrowing of the alveolar housing and palatal dehiscence were observed regardless of extraction following orthodontic treatment.

Fully automated determination of the cervical vertebrae maturation stages using deep learning with directional filters.

INTRODUCTION: We aim to apply deep learning to achieve fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. We propose an innovative custom-designed deep Convolutional Neural Network (CNN) with a built-in set of novel directional filters that highlight the edges of the Cervical Vertebrae in X-ray images. METHODS: A total of 1018 Cephalometric radiographs were labeled and classified according to the Cervical Vertebrae Maturation (CVM) stages. The images were cropped to extract the cervical vertebrae using an Aggregate Channel Features (ACF) object detector. The resulting images were used to train four different Deep Learning (DL) models: our proposed CNN, MobileNetV2, ResNet101, and Xception, together with a set of tunable directional edge enhancers. When using MobileNetV2, ResNet101 and Xception, data augmentation is adopted to allow adequate network complexity while avoiding overfitting. The performance of our CNN model was compared with that of MobileNetV2, ResNet101 and Xception with and without the use of directional filters. For validation and performance assessment, k-fold cross-validation, ROC curves, and p-values were used. RESULTS: The proposed innovative model that uses a CNN preceded with a layer of tunable directional filters achieved a validation accuracy of 84.63%84.63% in CVM stage classification into five classes, exceeding the accuracy achieved with the other DL models investigated. MobileNetV2, ResNet101 and Xception used with directional filters attained accuracies of 78.54%, 74.10%, and 80.86%, respectively. The custom-designed CNN method also achieves 75.11% in six-class CVM stage classification. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If the custom-designed CNN is used without the directional filters, the test accuracy decreases to 80.75%. In the Xception model without the directional filters, the testing accuracy drops slightly to 79.42% in the five-class CVM stage classification. CONCLUSION: The proposed model of a custom-designed CNN together with the tunable Directional Filters (CNNDF) is observed to provide higher accuracy than the commonly used pre-trained network models that we investigated in the fully automated determination of the CVM stages.

Assessment of Handicapping Labio-Lingual Deviation index scoring methods and their effect on orthodontic treatment coverage by Medicaid.

BACKGROUND: The purpose of this study was to assess the validity and reliability of Handicapping Labio-Lingual Deviation index (HLDI) scoring methods as calculated by digital models (DM) and visual inspection (VI) and their agreement to either meet or fail to meet the Medicaid coverage threshold. An additional objective was to assess the agreement with Medicaid managed care organizations (MCO) coverage decisions. METHODS: The study included the orthodontic records of 401 patients who applied for Medicaid coverage. Two methods were used to calculate HLDI scores: (1) Measurements derived from DMs using OrthoCAD software; and (2) VI of intraoral photographs. The levels of agreement between the two methods and the Medicaid coverage decision by a MCO were evaluated. RESULTS: The study results show a high level of agreement between the two HLDI calculation methods, DM and VI evaluation methods(Cramer's V = 0.812). The agreement on coverage decisions (eligible/not eligible) between VI methods and the official MCO decision was Cramer's V = 0.318. The agreement on coverage decisions between the DM method and the official MCO decision was Cramer's V = 0.318. CONCLUSIONS: MCO assessment results of the patients using HLDI showed low agreement with the results obtained by DM and VI scoring methods used in this study. The Illinois Medicaid system is apparently using unknown factors other than the HLDI score when determining when approving or disapproving orthodontic coverage. PRACTICAL IMPLICATIONS: MCO decisions on eligibility for orthodontic treatment coverage were not consistent with patients' treatment needs.

Alveolar cortical plate changes associated with incisor retraction and its influence on the limits of orthodontic tooth movement.

OBJECTIVE: The extent to which the modelling behaviour of the anterior alveolus limits tooth movement remains unclear. Will the labial and lingual cortical plates model as incisors retract, or will they remain unchanged, therefore limiting the extent of possible tooth movement? SETTING AND SAMPLE POPULATION: Pre- and post-treatment lateral cephalometric radiographs of 29 bimaxillary protrusive patients of South Korean descent were examined. Treatment consisted of two premolar extractions in one or both arches with en masse retraction of the incisors using miniscrew anchorage. MATERIALS AND METHODS: Pre- and post-treatment measurements of both tooth and cortical plate position were made at various increments along the length of the root and then compared using paired t tests. RESULTS: Despite the use of miniscrew anchorage, the incisors were retracted by controlled tipping. The labial cortical plates in both arches modelled to follow tooth movement. Following retraction of the incisors in the maxilla, the incisor root approached the lingual cortical plate, which remained unchanged. In the mandible, the lingual cortical plate position was unchanged except at the level closest to the cementoenamel junction. CONCLUSIONS: The maxillary and mandibular lingual cortical plates did not model to follow the incisor movement while the labial cortical plates did. These findings suggest that lingual cortical plates may act as limitations to planned orthodontic tooth movement.

Improving the accuracy of publicly available search engines in recognizing and classifying dental visual assets using convolutional neural networks.

AIM: To assess the accuracy of DigiBrain4, Inc (DB4) Dental Classifier and DB4 Smart Search Engine* in recognizing, categorizing, and classifying dental visual assets as compared with Google Search Engine, one of the largest publicly available search engines and the largest data repository. MATERIALS AND METHODS: Dental visual assets were collected and labeled according to type, category, class, and modifiers. These dental visual assets contained radiographs and clinical images of patients' teeth and occlusion from different angles of view. A modified SqueezeNet architecture was implemented using the TensorFlow r1.10 framework. The model was trained using two NVIDIA Volta graphics processing units (GPUs). A program was built to search Google Images, using Chrome driver (Google web driver) and submit the returned images to the DB4 Dental Classifier and DB4 Smart Search Engine. The categorical accuracy of the DB4 Dental Classifier and DB4 Smart Search Engine in recognizing, categorizing, and classifying dental visual assets was then compared with that of Google Search Engine. RESULTS: The categorical accuracy achieved using the DB4 Smart Search Engine for searching dental visual assets was 0.93, whereas that achieved using Google Search Engine was 0.32. CONCLUSION: The current DB4 Dental Classifier and DB4 Smart Search Engine application and add-on have proved to be accurate in recognizing, categorizing, and classifying dental visual assets. The search engine was able to label images and reject non-relevant results.

Digital Workflow for Combined Orthodontics and Orthognathic Surgery.

This article provides an overview of the digital workflow process for Combined orthodontics and Orthognathic surgery treatment starting from data acquisition (3-dimensional scanning, cone-beam computed tomography), data preparation, processing and Creation of a three-dimensional virtual augmented model of the head. Establishing a Proper Diagnosis and Quantification of the Dentofacial Deformity using 3D diagnostic model. Furthermore, performance of 3-dimensional Virtual orthognathic surgical treatment, and the construction of a surgical splint (via 3-dimensional printing) to allow transfer of the treatment plan to the actual patient during surgery.

Temporary Skeletal Anchorage Techniques.

As orthodontic treatment has advanced in complexity and in frequency, more recent techniques, using temporary skeletal anchorage, were developed to help correct more severe occlusal and dentofacial discrepancies that were treated with orthognathic surgery alone previously. These techniques have allowed the orthodontist to move teeth against a rigid fixation, allowing for more focused movements of teeth and for orthopedic growth modification. These types of treatments using rigid fixation have allowed for greater interaction between the orthodontist and the oral and maxillofacial surgeon, and have vastly enhanced the treatment planning for the orthodontist in today's society.

Accuracy of Dental Monitoring 3D digital dental models using photograph and video mode.

INTRODUCTION: This study aimed to test the accuracy of the 3-dimensional (3D) digital dental models generated by the Dental Monitoring (DM) smartphone application in both photograph and video modes over successive DM examinations in comparison with 3D digital dental models generated by the iTero Element intraoral scanner. METHODS: Ten typodonts with setups of class I malocclusion and comparable severity of anterior crowding were used in the study. iTero Element scans along with DM examination in photograph and video modes were performed before tooth movement and after each set of 10 Invisalign aligners for each typodont. Stereolithography (STL) files generated from the DM examinations in photograph and video modes were superimposed with the STL files from the iTero scans using GOM Inspect software to determine the accuracy of both photograph and video modes of DM technology. RESULTS: No clinically significant differences, according to the American Board of Orthodontics-determined standards, were found. Mean global deviations for the maxillary arch ranged from 0.00149 to 0.02756 mm in photograph mode and from 0.0148 to 0.0256 mm in video mode. Mean global deviations for the mandibular arch ranged from 0.0164 to 0.0275 mm in photograph mode and from 0.0150 to 0.0264 mm in video mode. Statistically significant differences were found between the 3D models generated by the iTero and the DM application in photograph and video modes over successive DM examinations. CONCLUSIONS: 3D digital dental models generated by the DM smartphone application in photograph and video modes are accurate enough to be used for clinical applications.

Three-dimensional assessment of soft tissue changes associated with bone-anchored maxillary protraction protocols.

INTRODUCTION: The aim of this study was to assess the 3-dimensional soft tissue changes in growing Class III patients with maxillary deficiency associated with 2 bone-anchored maxillary protraction protocols in relation to an untreated control group of Class III patients. METHODS: Growing skeletal Class III patients between the ages of 10 and 14 years participated in this study. In group 1 (n = 10), skeletally anchored facemasks were used with miniplates placed at the zygomatic buttress. In group 2 (n = 10), the patients were treated with Class III elastics extending from infrazygomatic miniplates in the maxilla to symphyseal miniplates in the mandible. Group 3 (n = 10) was an untreated control group. Three-dimensional stereophotogrammetry images were acquired before and after treatment, and then superimposed and analyzed. In addition, lateral cephalometric radiographs were analyzed. RESULTS: The maxilla moved forward significantly in groups 1 and 2 compared with the untreated control group (group 1, 4.87 mm; group 2, 5.81 mm). The 3-dimensional soft tissue analysis showed significant treatment effects; the major changes were observed in the upper lips, cheeks, and middle of the face, which had a significant positive sagittal displacement in both treatment groups. The lower lip and chin area showed significant negative sagittal changes that indicated that the soft tissue growth in this area was restrained with backward displacement especially in group 1 more than in group 2. CONCLUSIONS: The 2 bone-anchored maxillary protraction protocols effectively improved the Class III concave soft tissue profile.