Does Maxillomandibular Fixation Technique Affect Occlusion Quality in Segmental LeFort I Osteotomy?

BACKGROUND: Segmental maxillary osteotomies require precise occlusal control due to variability in individual segment positioning. The role of maxillomandibular fixation (MMF) technique on occlusal control has not been validated. PURPOSE: The purpose is to measure and compare the accuracy of occlusal positioning among MMF techniques. STUDY DESIGN, SETTING, SAMPLE: This was a double-blinded in vitro study on experiment models to simulate a 3-piece LeFort I osteotomy. The models were constricted posteriorly and expanded using 3 different MMF techniques and compared to the unaltered baseline occlusion. Based on sample size calculation, 32 separate attempts were made for each MMF technique. PREDICTOR VARIABLE: The predictor variable was MMF technique (brackets, MMF screws, and embrasure wires). MAIN OUTCOME VARIABLES: The primary outcome variable was the visual occlusal analysis score, a 1.00 to 4.00 continuous scale measuring the similarity of the achieved occlusion to the planned (control) occlusion assessed by an oral and maxillofacial surgeon and an orthodontist. High visual occlusal analysis score indicated greater occlusal accuracy, with 3.50 defined as the threshold for accuracy. The secondary outcome variable was the linear error of the achieved occlusion at the canine and first molar teeth, with lower error indicating greater accuracy. An a priori accuracy threshold of 0.5 mm was set for this variable. COVARIATES: None. ANALYSES: Kruskal-Wallis test with post hoc testing was used to analyze the difference in the outcome variables of interest. P value < .05 was considered statistically significant. RESULTS: Thirty-two attempts for each technique showed that brackets had higher VAOS than MMF screws and embrasure wires (median differences 1.49 and 0.48, P < .001), and had lower linear occlusal error (median differences 0.35 to 0.99 mm, P < .001). CONCLUSION AND RELEVANCE: MMF technique influences the quality of occlusal control, with greater visual rating scores and lower linear errors seen with brackets than with embrasure wires or MMF screws.

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.

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.

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.

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.

COVID-19 infection rates and mitigation strategies in orthodontic practices.

BACKGROUND: COVID-19 has impacted and increased risks for all populations, including orthodontic patients and providers. It also changes the practice management and infection control landscape in the practices. This study aimed to investigate the COVID-19 infection and vaccination status of orthodontic providers and mitigation approaches in orthodontic practices in the United States during 2021. METHODS: A validated 50-question research electronic data capture (REDCap) browser-based questionnaire was distributed to 12,393 orthodontists and pediatric dentists who reported actively providing orthodontic treatment. Questions were designed to collect demographic data of respondents, evaluate the COVID-19 mitigation approaches, and evaluate the history of COVID-19 infection and vaccination status of the orthodontic providers. Associations of demographic and the COVID-19 mitigation approaches were assessed using chi-square tests at the significance level of 0.05. RESULTS: Four hundred fifty-seven returned the survey (response rate 3.69%) for analysis. Most respondents were vaccinated, and increased infection control measures in response to the pandemic. Half of the respondents practiced teledentistry and switched to digital impression systems. Two-thirds reported difficulties in attaining PPEs due to the increased cost and scarcity of PPEs. About 6% of respondents reported a history of COVID-19 infection, and 68.9% of their staff had COVID-19 infection. Statistically significant associations were found between increased practice experience with difficulties in acquiring PPE (p = .010). There were no significant associations between races of respondents, geographic location, and years of practicing when cross-tabulated with vaccination status or COVID-19 infection rate (p > .05). CONCLUSION: Increased infection control strategies were employed in almost all orthodontic practices in addition to existing universal precaution. Most of the orthodontic providers and their staff members were vaccinated. While staff's infection rates were an issue, doctors' infection rates remained low.

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.

Assessment of automatic cephalometric landmark identification using artificial intelligence.

OBJECTIVE: To compare the accuracy of cephalometric landmark identification between artificial intelligence (AI) deep learning convolutional neural networks (CNN) You Only Look Once, Version 3 (YOLOv3) algorithm and the manually traced (MT) group. SETTING AND SAMPLE POPULATION: The American Association of Orthodontists Federation (AAOF) Legacy Denver collection was used to obtain 110 cephalometric images for this study. MATERIALS AND METHODS: Lateral cephalograms were digitized and traced by a calibrated senior orthodontic resident using Dolphin Imaging. The same images were uploaded to AI software Ceppro DDH Inc The Cartesian system of coordinates with Sella as the reference landmark was used to extract x- and y-coordinates for 16 cephalometric points: Nasion (Na), A point, B point, Menton (Me), Gonion (Go), Upper incisor tip, Lower incisor tip, Upper incisor apex, Lower incisor apex, Anterior Nasal Spine (ANS), Posterior Nasal Spine (PNS), Pogonion (Pg), Pterigomaxillary fissure point (Pt), Basion (Ba), Articulare (Art) and Orbitale (Or). The mean distances were assessed relative to the reference value of 2 mm. Student paired t-tests at significance level of P < .05 were used to compare the mean differences in each of the x- and y-components. SPSS (IBM-vs. 27.0) software was used for the data analysis. RESULTS: There was no statistical difference for 12 out of 16 points when analysing absolute differences between MT and AI groups. CONCLUSION: AI may increase efficiency without compromising accuracy with cephalometric tracings in routine clinical practice and in research settings.

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.

Changes in sleep and airway variables in patients with obstructive sleep apnea after mandibular advancement splint treatment.

INTRODUCTION: Obstructive sleep apnea (OSA) is an extensive public health problem that imposes considerable morbidity. Mandibular advancement splint (MAS) therapy is a well tolerated treatment, but success rates are difficult to predict. Our objective was to investigate the relationship of oropharyngeal airway dimensions, sleep characteristics, patient biometrics, and treatment response within an OSA patient sample. METHODS: Records of 33 adults were assessed retrospectively with the use of Dolphin 3D and Image J to measure the airway on pretreatment supine cone-beam computed tomography images and derived lateral cephalograms. The patients used Somnodent (Somnomed; Crows Nest, Australia) MAS appliances, which were titrated over 6-8 weeks. Appliance titration measurements and pre- and posttreatment polysomnograms were assessed. Respiratory disturbance index (RDI), absolute and percentage changes in RDI, non-rapid eye movement (NREM) RDI, rapid eye movement (REM) RDI, supine and nonsupine NREM and REM RDI, and minimal blood-oxygen saturation variables were evaluated. The associations of measurements from 2D and 3D minimal anterior-posterior linear distance and 3D airway variables with MAS treatment response were estimated. RESULTS AND CONCLUSIONS: Combined effects of baseline total airway volume, body mass index, neck circumference, location of minimal cross sectional area, and OSA severity were associated with treatment response. Patients with higher initial OSA and more superiorly located airway constriction showed enhanced treatment response to MAS therapy. Airway constriction due to maxillofacial disproportions rather than soft tissue obstruction also showed better treatment response. No significant relationships were found in lateral cephalogram measurements.