Experienced Orthodontists Research Articles

Accuracy of Orthodontic Anchor Screw Placement Using a 3D-Printed Surgical Guide.

Background Although radiographs and computed tomography (CT) images are reviewed before temporary anchorage device (TAD) implantation, implantation of TADs exactly as planned is difficult. This study aimed to evaluate the accuracy of TAD implantation using an original surgical guide fabricated using cone-beam CT data and computer-aided design software. Methodology The studyparticipants included six experienced orthodontists who had implanted ≥20 TADs, and six inexperienced orthodontists who had never implanted a TAD. Maxillary dental typodont models with radiopaque tooth crowns and roots were used. A total of four TADs were implanted on the buccal sides: between the second bicuspid and first molars and between the first and second molars bilaterally. The accuracy of TAD implantation was examined in two groups: in 12 dental typodont models, TAD implantation was performed using a surgical guide (guide group), and in 12 dental typodont models, TAD implantation was performed without a surgical guide (freehand group). All dental typodont models implanted a total of 96 TADs.The TAD position was evaluated using the CT coordinate system and 3D image measurement software. Using the long axis of the TAD as a reference, the distance between the coronal and apical ends of the implanted TAD and those of the planned TAD, i.e., the ideal implantation position, was measured in both groups along the x, y, and z axes. The medians of the values were compared between the groups. Additionally, the presence of root contact was compared between the experienced and inexperienced orthodontists. Results On the x-axis, the linear deviations (median) of the coronal and apical ends of the TAD in the freehand group were 1.06 mm and 1.36 mm, respectively. In contrast, in the guide group, the deviations were 0.65 mm and 0.90 mm, respectively, and the difference was statistically significant (p = 0.002 and p = 0.005, respectively). On the y-axis, the deviations in the freehand group were 1.13 mm and 1.08 mm, respectively. In contrast, the deviations in the guide group were 0.71 mm and 0.79 mm, respectively, and only the coronal deviations were significantly different between the groups (p = 0.006). On the z-axis, the deviations in the freehand group were 1.44 mm and 1.86 mm, respectively. In contrast, the deviations in the guide group were 0.75 mm and 1.16 mm, respectively, and the difference was statistically significant (p = 0.006 and p = 0.002, respectively). Conclusions The use of a surgical guide allowed for more accurate TAD implantation. Additionally, TAD implantation using a guide prevented root damage.

Evaluation of Artificial Intelligence as a Search Tool for Patients: Can ChatGPT-4 Provide Accurate Evidence-Based Orthodontic-Related Information?

Artificial intelligence (AI) is already a part of our reality. Many people started using ChatGPT in their daily life, replacing existing web browsers. The confidence people put in the ability of ChatGPT to provide accurate medical information is increasing. With that, the need for proper assessment tools for the safety and reliability of ChatGPT is also crucial. This study aimed to assess the accuracy, reliability, andquality of information provided by ChatGPT-4 on three specific orthodontic topics, namely, impacted canines, interceptive orthodontic treatment, and orthognathic surgery, as evaluated by five experienced orthodontists using a Likert scale ranking method. Using ChatGPT version 4, 20 most commonly asked questions were generated and answered on the following topics: impacted canines, interceptive treatment, and orthognathic surgery. The evaluation of the quality of the answers provided was done by five experienced orthodontists. Quality assessment was done using the Likert scale ranking method. The quality answers generated by a conversational AI system (ChatGPT4) were evaluated by five experienced orthodontists for three topics: impacted canines, interceptive orthodontics, and orthognathic surgery. The evaluators rated each question-answer pair on a five-point scale from "very poor" to "very good." The results showed that the AI system produced generally good quality information for all topics, with no significant difference between them. The inter-rater agreement among the experts was low, indicating some variability in their judgments. This study demonstrates that ChatGPT4 can provide generally good information on impacted canines, interceptive treatment, and orthognathic surgery. However, answers provided should be handled with caution due to variability and lack of reliability and should not be considered a substitute for professional opinion.

Evaluation of different machine learning algorithms for extraction decision in orthodontic treatment.

The extraction decision significantly affects the treatment process and outcome. Therefore, it is crucial to make this decision with a more objective and standardized method. The objectives of this study were (1) to identify the best-performing model among seven machine learning (ML) models, which will standardize the extraction decision and serve as a guide for inexperienced clinicians, and (2) to determine the important variables for the extraction decision. This study included 1000 patients who received orthodontic treatment with or without extraction (500 extraction and 500 non-extraction). The success criteria of the study were the decisions made by the four experienced orthodontists. Seven ML models were trained using 36 variables; including demographic information, cephalometric and model measurements. First, the extraction decision was performed, and then the extraction type was identified. Accuracy and area under the curve (AUC) of the receiver operating characteristics (ROC) curve were used to measure the success of ML models. The Stacking Classifier model, which consists of Gradient Boosted Trees, Support Vector Machine, and Random Forest models, showed the highest performance in extraction decision with 91.2% AUC. The most important features determining extraction decision were maxillary and mandibular arch length discrepancy, Wits Appraisal, and ANS-Me length. Likewise, the Stacking Classifier showed the highest performance with 76.3% accuracy in extraction type decisions. The most important variables for the extraction type decision were mandibular arch length discrepancy, Class I molar relationship, cephalometric overbite, Wits Appraisal, and L1-NB distance. The Stacking Classifier model exhibited the best performance for the extraction decision. While ML models showed a high performance in extraction decision, they could not able to achieve the same level of performance in extraction type decision.

Influence of the design of 3D-printed indirect bonding trays and experience of the clinician on the accuracy of bracket placement.

The aim was to investigate the influence of three different three-dimensional (3D)-printed bonding tray designs and professional experience on accuracy of indirect bracket placement. Virtual bracket placement was performed on ascanned dental model using OnyxCeph software (Image Instruments, Chemnitz, Germany). Three different designs for indirect bonding trays (open, semi-open, and closed design) were created and produced using a3D printer. To analyze the influence of professional experience, one of the three tray designs was produced twice. In this case, bracket placement was performed by an inexperienced dentist. Bracket positions were scanned after the indirect bonding procedure. Linear and angular transfer errors were measured. Significant differences between the target and actual situation were analyzed using the Kruskal-Wallis and χ2 test. All bonding tray designs resulted in comparable results. The results of the unexperienced dentist showed significantly higher deviations than those for the experienced orthodontist in the torque direction. However, the mean values were comparable. The open tray design exceeded the clinically acceptable limits of 0.25 mm and 1° more often. The inexperienced dentist exceeded these limits significantly more often than the experienced orthodontist in the vertical and torque direction. The immediate bracket loss rate showed no significant differences between the different tray designs. Significantly more bracket losses were observed for the inexperienced dentist during the procedure compared to the experienced orthodontist. The bonding tray design and professional experience had an influence on the exceedance of clinically relevant limits of positioning accuracy and on the bracket loss rate.

An evaluation of orthodontic information quality regarding artificial intelligence (AI) chatbot technologies: A comparison of ChatGPT and google BARD

Abstract Introduction In recent times, chatbots have played an increasing and noteworthy role in the field of medical practice. The present research was conducted to evaluate the accuracy of the responses provided by ChatGPT and BARD, two of the most utilised chatbot programs, when interrogated regarding orthodontics. Materials and methods Twenty-four popular questions about conventional braces, clear aligners, orthognathic surgery, and orthodontic retainers were chosen for the study. When submitted to the ChatGPT and Google BARD platforms, an experienced orthodontist and an orthodontic resident rated the responses to the questions using a five-point Likert scale, with five indicating evidence-based information, four indicating adequate information, three indicating insufficient information, two indicating incorrect information, and one indicating no response. The results were recorded in Microsoft Excel for comparison and analysis. Results No correlation was found between the ChatGPT and Google BARD scores and word counts. However, a moderate to significant relationship was observed between the scores and several listed references. No significant association was found between the number of words and references, and a statistically significant difference was observed in both investigators’ numerical rating scales using the AI tools (p = 0.014 and p = 0.030, respectively). Conclusion Generally, ChatGPT and BARD provide satisfactory responses to common orthodontic inquiries that patients might ask. ChatGPT’s answers marginally surpassed those of Google BARD in quality.

Soft skills in Orthodontics: an analysis in residents and experienced professionals.

Soft skills represent a range of personal skills, attitudes and characteristics relevant to success and adequate work performance. This study aimed to evaluate the knowledge and usage of soft skills in Orthodontics. The participants answered a questionnaire containing 27 objective questions on awareness and frequency of soft skills in their professional activities. Participants were also asked to rank the soft skills in a crescent order of importance. The sample was divided into subgroups: A) residents in Orthodontics; B) orthodontists with less than 5 years of experience and C) orthodontists with more than 5 years of experience. Intergroup comparisons were performed using the Kruskal-Wallis test. Sexual differences were compared using Mann-Whitney test (p< 0.05). The sample of this observational study comprised 129 experienced orthodontists and residents in Orthodontics (92 women, 37 men) with mean age of 35.3 years. From the total sample, 54,6% of respondents reported no previous instructions on soft skills. All respondents reported using the analyzed soft skills with a similar frequency (median 4-5). Residents reported accessing reliable sources in bibliographic research less frequently (46%). Female orthodontists reported to seek help from teachers and other professionals more often than males. Ethics and communication were frequently ranked as the most important soft skills. Information management and leadership were frequently less ranked as important soft skills. Poor knowledge of soft skills was demonstrated by residents and orthodontists. Communication skill was highly used and frequently ranked as the most important soft skill.

Facial profile esthetics and its correlation with cephalometric measurements in Class II patients before and after functional orthodontic treatment: perception of orthodontists and laypersons

Orthodontists heavily rely on cephalometric measurements to evaluate the esthetic outcomes of orthodontic treatments, while patients’ assessments of profile attractiveness are influenced by their personal esthetic preferences. The objective of this study was to compare facial esthetic evaluations between orthodontists and laypersons, examine the correlation between cephalometric measurements and assessments of facial attractiveness, and identify the cephalometric measurements that are most associated with profile attractiveness in teenage Class II patients. This study included 28 patients (17 boys and 11 girls, mean age of 11.83 ± 1.48 years) who fulfilled the inclusion criteria and were treated with activator appliances. An experienced orthodontist conducted objective cephalometric analyses before and after the treatment. The standard profiles of patients, pre- and post-treatment, were independently evaluated by 10 orthodontists and 20 laypersons. Pearson correlation analysis was used to analyze the relationships between objective cephalometric values and subjective facial esthetic ratings. The results indicated that laypersons generally assigned lower ratings to facial attractiveness compared to orthodontists. Significant differences in evaluations between orthodontists and laypersons were noted in the ratings of the upper lip position before treatment and the ratings of overall attractiveness, lower lip position and chin position after treatment. In addition, there were substantial correlations between objective measurements and the subjective scores provided by orthodontists. In contrast, the esthetic evaluations by laypersons correlated with only a limited set of objective measurements. The positions of the upper and lower incisors, the relationship of the upper and lower lip to E-line, and the Mentocervical Angle showed significant correlations with esthetic scores. In conclusion, the study reveals that laypersons were more critical of facial profiles compared to orthodontists. Notably, the positions of the upper and lower incisors, the E-line and the Mentocervical Angle had a significant impact on facial esthetics, highlighting their importance in evaluating orthodontic outcomes.

Convolutional neural network-assisted diagnosis of midpalatal suture maturation stage in cone-beam computed tomography

ObjectivesThe selection of treatment for maxillary expansion is closely related to the calcification degree of the midpalatal suture. A classification method for individual assessment of the morphology of midpalatal suture in cone-beam computed tomography (CBCT) is useful for evaluating the calcification degree. Currently, convolutional neural networks (CNNs) have been introduced into the field of oral and maxillofacial imaging diagnosis. This study validated the ability of CNN models in assessing the maturation stage of the midpalatal suture. MethodsThe existing CNN model ResNet50 was trained to locate the CBCT transverse plane which contained a complete midpalatal suture. ResNet18, ResNet50, RessNet101, Inception-v3, and Efficientnetv2-s models were trained to evaluate the midpalatal suture maturation stage. Multi-class classification metrics, accuracy, recall, precision, F1-score, and area under the curve values from the receiver operating characteristic curve were used to evaluate the performance of the models, and gradient-weighted class activation map technology was utilised to visualise five midpalatal suture maturation stages for each model. ResultsResnet50 demonstrated an accuracy of 99.74 % in identifying the transverse plane that contained the complete midpalatal suture. The highest accuracies achieved on the two-stage, three-stage, and five-stage maturation classification tests were 95.15, 88.06, and 75.37 %, all of which exceeded the average accuracy of three experienced orthodontists. ConclusionsThe CNN model can locate the plane of the midpalatal suture in CBCT images and can assist clinicians in assessing the maturation stage of the midpalatal suture to select the means of maxillary expansion. Clinical significanceThe application of artificial intelligence on CBCT midpalatal suture plane localisation and maturation stage evaluation enhances diagnostic and treatment efficiency and accuracy of individual assessment of midpalatal suture calcification degree. Additionally, it assists the clinical palatal expansion technique in achieving ideal results.

Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs.

The purpose of the present clinical study was to compare the Ricketts and Steiner cephalometric analysis obtained by two experienced orthodontists and artificial intelligence (AI)-based software program and measure the orthodontist variability. A total of 50 lateral cephalometric radiographs from 50 patients were obtained. Two groups were created depending on the operator performing the cephalometric analysis: orthodontists (Orthod group) and an AI software program (AI group). In the Orthod group, two independent experienced orthodontists performed the measurements by performing a manual identification of the cephalometric landmarks and a software program (NemoCeph; Nemotec) to calculate the measurements. In the AI group, an AI software program (CephX; ORCA Dental AI) was selected for both the automatic landmark identification and cephalometric measurements. The Ricketts and Steiner cephalometric analyses were assessed in both groups including a total of 24 measurements. The Shapiro-Wilk test showed that the data was normally distributed. The t-test was used to analyze the data (α = 0.05). The t-test analysis showed significant measurement discrepancies between the Orthod and AI group in seven of the 24 cephalometric parameters tested, namely the corpus length (p = 0.003), mandibular arc (p < 0.001), lower face height (p = 0.005), overjet (p = 0.019), and overbite (p = 0.022) in the Ricketts cephalometric analysis and occlusal to SN (p = 0.002) and GoGn-SN (p < 0.001) in the Steiner cephalometric analysis. The intraclass correlation coefficient (ICC) between both orthodontists of the Orthod group for each cephalometric measurement was calculated. Significant discrepancies were found in seven of the 24 cephalometric measurements tested between the orthodontists and the AI-based program assessed. The intra-operator reliability analysis showed reproducible measurements between both orthodontists, except for the corpus length measurement. The artificial intelligence software program tested has the potential to automatically obtain cephalometric analysis using lateral cephalometric radiographs; however, additional studies are needed to further evaluate the accuracy of this AI-based system.

Growth Outcomes Audit for Unilateral Cleft Lip and Palate (UCLP) After 2-stage Palate Repair.

To assess the impact of 1 and 2-stage palatoplasty protocol on the dental arch relationships in unilateral cleft lip and palate (UCLP) in a single center. Our study consisted of 349 individuals divided into 2 groups according to the palatoplasty protocol. Two-stage group comprised 169 subjects with UCLP (mean age: 6.9y, 110 male and 59 female) who underwent lip, nasal ala, and anterior palate repair with vomer flap from 3 to 6 months (first surgery stage). Soft palate repair occurred from 12 to 18 months (second surgery stage). The one-stage group comprised 180 subjects with UCLP (mean age: 7.2y, 108 male and 72 female) who underwent 1-stage palatoplasty. Dental models were evaluated by 3 experienced orthodontists applying Goslon Yardstick and the 5-year-old index (FYOI). The influence of the palatoplasty technique and surgeon factor on the interarch relationship was evaluated. The weighted Kappa was used to assess intraexaminer and interexaminer agreements for comparisons of dental arch relationships. Intergroup comparisons were conducted using the χ 2 test ( P <0.05). The intraexaminer reliability was very good (0.81 to 0.98) and interexaminer reliability varied from satisfactory to very good (0.56 to 0.83). The mean occlusal index of the 2-stage and 1-stage groups was 2.77 and 3.03, respectively. The variability of the mean index between surgeons varied from 2.38 to 3.2 in the 2-stage group and 2.91 to 3.2 in the 1-stage group. There were significant differences in the frequency of Goslon 5 index ( P =0.002) between groups, with the 2-stage group presenting less cases (1.18%) than the group 1-stage (11.11%). The interarch relationship was similar for both palate repair protocols. Two-stage palatoplasty showed a decreased prevalence of Goslon index 5.

Precision and Accuracy Assessment of Cephalometric Analyses Performed by Deep Learning Artificial Intelligence with and without Human Augmentation

The aim was to assess the precision and accuracy of cephalometric analyses performed by artificial intelligence (AI) with and without human augmentation. Four dental professionals with varying experience levels identified 31 landmarks on 30 cephalometric radiographs twice. These landmarks were re-identified by all examiners with the aid of AI. Precision and accuracy were assessed by using intraclass correlation coefficients (ICCs) and mean absolute errors (MAEs). AI revealed the highest precision, with a mean ICC of 0.97, while the dental student had the lowest (mean ICC: 0.77). The AI/human augmentation method significantly improved the precision of the orthodontist, resident, dentist, and dental student by 3.26%, 2.17%, 19.75%, and 23.38%, respectively. The orthodontist demonstrated the highest accuracy with an MAE of 1.57 mm/°. The AI/human augmentation method improved the accuracy of the orthodontist, resident, dentist, and dental student by 12.74%, 19.10%, 35.69%, and 33.96%, respectively. AI demonstrated excellent precision and good accuracy in automated cephalometric analysis. The precision and accuracy of the examiners with the aid of AI improved by 10.47% and 27.27%, respectively. The AI/human augmentation method significantly improved the precision and accuracy of less experienced dental professionals to the level of an experienced orthodontist.

The Prevalence and Categories of Impacted Maxillary Canines: A Radiographic Study.

The current study aims to identify the prevalence of impacted maxillary canines among the Saudi population in the Qassim region. A total of 6,946 panoramic radiographs were collected retrospectively and screened by an experienced orthodontist to determine the frequency of impacted maxillary canines. The Statistical Package for the Social Sciences (SPSS) (IBM SPSS Statistics, Armonk, NY, USA) was used to evaluate the significant differences between categorical variables such as gender and the position of impacted teeth. Overall, 4,977 patients were included in the final analysis. There were 2,509 (50.4%) males and 2,468 (49.6%) females. The prevalence of impacted maxillary canines in our sample was 2.7%, with a higher prevalence of impacted maxillary canines in males (n=74, 2.94%) compared to females (n=60, 2.43%). The majority of the impacted canines were unilateral (n=105, 78.4%) compared to bilateral (n=29, 21.6%). Impacted maxillary canines were found in 134 out of 4,977 (2.7%) patients. Males (2.94%) demonstrated a higher impaction rate than females (2.43%). However, the difference was not statistically significant.

Automatic determination of pubertal growth spurts based on the cervical vertebral maturation staging using deep convolutional neural networks.

This study aimed to develop a deep convolutional neural network (CNN) for automatic classification of pubertal growth spurts using cervical vertebral maturation (CVM) staging based on the lateral cephalograms of an Iranian subpopulation. Cephalometric radiographs were collected from 1846 eligible patients (aged 5-18 years) referred to the orthodontic department of Hamadan University of Medical Sciences. These images were labeled by two experienced orthodontists. Two scenarios, including two- and three-class (pubertal growth spurts using CVM), were considered as the output for the classification task. The cropped image of the second to fourth cervical vertebrae was used as input to the network. After the preprocessing, the augmentation step, and hyperparameter tuning, the networks were trained with initial random weighting and transfer learning. Finally, the best architecture among the different architectures was determined based on the accuracy and F-score criteria. The CNN based on the ConvNeXtBase-296 architecture had the highest accuracy for automatically assessing pubertal growth spurts based on CVM staging in both three-class (82% accuracy) and two-class (93% accuracy) scenarios. Given the limited amount of data available for training the target networks for most of the architectures in use, transfer learning improves predictive performance. The results of this study confirm the potential of CNNs as an auxiliary diagnostic tool for intelligent assessment of skeletal maturation staging with high accuracy even with a relatively small number of images. Considering the development of orthodontic science toward digitalization, the development of such intelligent decision systems is proposed.

Comparative assessment of treatment efficiency and patient experience between Dental Monitoring and conventional monitoring of clear aligner therapy: A single-center randomized controlled trial

This 2-arm randomized controlled clinical trial (RCT) aimed to assess the effect of Dental Monitoring (DM) on the efficiency of clear aligner therapy (CAT) and patient experience compared with conventional monitoring (CM) used for regularly scheduled clinical appointments. Fifty-six patients with full permanent dentition treated with CAT participated in this RCT. Patients were recruited from a single private practice and treated by 1 experienced orthodontist. Randomization was performed with permutated blocks of 8 patients assigned to either a CM or DM group with allocations concealed in opaque, sealed envelopes. It was not feasible to blind subjects or investigators. The primary treatment efficiency outcome assessed was the number of appointments. Secondary outcomes included the time to reach the first refinement, the number of refinements, the total number of aligners, and treatment duration. The patient experience was assessed using a visual analog scale questionnaire administered at the end of CAT. No patients were lost to follow-up. There was no significant difference in the number of refinements (mean= 0.1; 95% confidence interval [CI], -0.2 to 0.5; P= 0.43) and the number of total aligners (median= 5; 95% CI, -1 to 13; P= 0.09). There was a significant difference in the number of appointments, with the DM group requiring 1.5 fewer visits (95% CI, -3.3, -0.7; p= 0.02) as well as overall treatment duration, with the DM group taking 1.9 months longer (95% CI, 0.0-3.6; P= 0.04). There was a difference between study groups regarding the importance of face-to-face appointments, with the DM group not perceiving face-to-face appointments as important (P= 0.03). DM with CAT resulted in 1.5 fewer clinical appointments and a longer treatment duration of 1.9 months. There were no significant intergroup differences in the number of refinements or total aligners. CM and DM groups had similarly high levels of satisfaction with CAT. The trial was registered at Australian New Zealand Clinical Trials Registry (ACTRN12620000475943). The protocol was published before trial commencement. This research did not receive any grant from funding agencies.

Association between arch width changes and long-term stability 20 years after orthodontic treatment with and without extractions.

To investigate long-term stability 20 years after orthodontic treatment and the association with arch width changes during treatment. This retrospective study investigated 103 patients with Class I and II malocclusions treated with fixed appliances with and without extractions. The sample was treated by one experienced orthodontist and collected from a private orthodontic office. Dental casts were obtained pretreatment (T1), posttreatment (T2), and long-term postretention (T3); they were scanned and converted to STL files. Measurements were evaluated in for the upper and lower arch: intercanine width (IC), intermolar (IM) width, Little's irregularity index (LII). There were 73 female and 30 male patients. Class I was present in 74 patients and Class II in 29. Average postretention time was 17.2 (±6.5) years after an average active retention time of 3.4 (±1.17) years. Extraction was performed in 55 patients while 48 received nonextraction treatment. Bonferroni Post Hoc test showed that LII in the upper and lower arches at T1 was significantly higher in the extraction group (P < .001). Upper and lower arch LII at T3 was slightly higher in extraction cases but remained under 2.05 mm. LII at T3 in the upper and lower arches showed negative correlation with IM T3 in the upper arch (Pearson, N = 103, P = .047), while IC in the upper and lower arches at T3 correlated with IM T3 in the upper and lower (N = 103, P < .001). Clinically relevant long-term stability in both arches was found in extraction and nonextraction cases. Intermolar width and its change during orthodontic treatment was an influential factor on long-term stability in extraction cases.

Evaluation of scientific quality of YouTube video content related to orthodontic pain management.

With the abundant use of the internet, patients undergoing or interested in orthodontic treatment try to use it to obtain information on pain during treatment. However, YouTube™ is unregulated and may potentially contain inaccurate information. Thus, this study aimed to evaluate the scientific quality of the videos on YouTube™ related to orthodontic pain management. A total of 62 videos related to orthodontic pain management were included in the study. All videos were evaluated by two experienced orthodontists. The video uploader, content, length, upload date, time since upload, number of views, comments, likes, dislikes, Interaction index, and Viewing rate of the videos were recorded and evaluated. The videos were scored using the Quality Criteria for Consumer Health Information (DISCERN), Global Quality Scale (GQS), and Audio-Visual Quality (AVQ), and divided into two groups: Doctors and Non-doctors. The mean DISCERN score was 2.56 ± 0.91, the GQS score was 2.56 ± 1.06, and AVQ was 2.48 ± 0.68. A statistically significant difference was found in DISCERN score of videos uploaded by Doctors compared to Non-doctors, but no statistically significant difference was found in GQS and AVQ scores between both groups (p> 0.05). The videos uploaded by Doctors were better in terms of quality and reliability, as compared to Non-doctors; and the AVQ of the videos uploaded by both groups was adequate. Despite that, both groups did not serve as a good source of information. YouTube™ cannot be considered a reliable source of information in terms of quality and reliability on videos related to orthodontic pain management.

Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning—A Review

Artificial intelligence (AI) is a new breakthrough in technological advancements based on the concept of simulating human intelligence. These emerging technologies highly influence the diagnostic process in the field of medical sciences, with enhanced accuracy in diagnosis. This review article intends to report on the trends and application of AI models designed for diagnosis and treatment planning in orthodontics. A data search for the original research articles that were published over the last 22 years (from 1 January 2000 until 31 August 2022) was carried out in the most renowned electronic databases, which mainly included PubMed, Google Scholar, Web of Science, Scopus, and Saudi Digital Library. A total of 56 articles that met the eligibility criteria were included. The research trend shows a rapid increase in articles over the last two years. In total: 17 articles have reported on AI models designed for the automated identification of cephalometric landmarks; 12 articles on the estimation of bone age and maturity using cervical vertebra and hand-wrist radiographs; two articles on palatal shape analysis; seven articles for determining the need for orthodontic tooth extractions; two articles for automated skeletal classification; and 16 articles for the diagnosis and planning of orthognathic surgeries. AI is a significant development that has been successfully implemented in a wide range of image-based applications. These applications can facilitate clinicians in diagnosing, treatment planning, and decision-making. AI applications are beneficial as they are reliable, with enhanced speed, and have the potential to automatically complete the task with an efficiency equivalent to experienced clinicians. These models can prove as an excellent guide for less experienced orthodontists.

Artificial intelligence-based algorithm for cervical vertebrae maturation stage assessment.

The aim of this study was to develop an artificial intelligence (AI) algorithm to automatically and accurately determine the stage of cervical vertebra maturation (CVM) with the main purpose being to eliminate the human error factor. Archives of the cephalometric images were reviewed and the data of 1501 subjects with fully visible cervical vertebras were included in this retrospective study. Lateral cephalometric (LC) that met the inclusion criteria were used in the training process, labeling was carried out using a computer vision annotation tool (CVAT), tracing was done by an experienced orthodontist as a gold standard and, in order to limit the effect of the uneven distribution of the training data set, maturation stage was classified with a modified Bachetti method by the operator who labelled them. The labelled data were split randomly into a training set (80%), a testing set (10%) and an validation set (10%), to measure intra-observer, inter-observer reliability, intraclass correlation coefficient (ICC) and weighted Cohen's kappa test was carried out. The ICC was valued at 0.973, weighted Cohen's kappa standard error was 0.870 ± 0.027 which shows high reliability of the observers and excellent level of agreement between them, the segmentation network achieved a global accuracy of 0.99 and the average dice score overall images was 0.93. The classification network achieved an accuracy of 0.802, class sensitivity of (pre-pubertal 0.78; pubertal 0.45; post-pubertal 0.98), respectively, per class specificity of (pre-pubertal 0.94; pubertal 0.94; post-pubertal 0.75), respectively. The developed algorithm showed the ability to determine the cervical vertebrae maturation stage which might aid in a faster diagnosis process by eliminating human intervention, which might lead to wrong decision-making procedures that might affect the outcome of the treatment plan. The developed algorithm proved reliable in determining the pre-pubertal and post-pubertal growth stages with high accuracy.

Development and validation of a new prognostic tool for impacted maxillary canines, 'Easy box': A cone-beam computed tomographic study.

To develop a tool to facilitate simple, easy and rapid assessment of the level of difficulty in disimpaction of an impacted canine using cone-beam computed tomography (CBCT). A cross-sectional study. A panel of four experienced orthodontists formulated a questionnaire capable of portraying the limits of a three-dimensional (3D) area around impacted maxillary canines on CBCT images, which would determine the disimpaction difficulty level. In total, 14 experienced orthodontists graded each item in the questionnaire and assigned scores in the range of 0-5. Average scores obtained outlined the limits of a 3D area, within which an impaction was deemed favourable. This area was termed 'Easy box'. The disimpaction difficulty level of 170 impacted maxillary canines was then assessed using 'Easy box' and the existing tool KPG index for convergent validation. Perfect inter-examiner agreement was obtained as evidenced by Fleiss kappa value of 0.984 for the tool 'Easy box' and 0.948 for the KPG index. There was also perfect agreement between the two methods of assessment (Cohen weighted kappa, k>0.86). The average time taken for assessment using 'Easy box' was 3.3 min compared to 8.8 min for the KPG index. 'Easy box' proves to be a simple, accurate and rapid assessment tool for the prognostic evaluation of impacted canines, using CBCT images. Impacted maxillary canines with cusp tips that lie beyond the limits of the 3D 'Easy box' area with a beta angle greater than 45°, were rated to be more difficult to align.

Deep Learning–Based Prediction of the 3D Postorthodontic Facial Changes

With the increase of the adult orthodontic population, there is a need for an accurate and evidence-based prediction of the posttreatment face in 3 dimensions (3D). The objectives of this study are 1) to develop a 3D postorthodontic face prediction method based on a deep learning network using the patient-specific factors and orthodontic treatment conditions and 2) to validate the accuracy and clinical usability of the proposed method. Paired sets (n = 268) of pretreatment (T1) and posttreatment (T2) cone-beam computed tomography (CBCT) of adult patients were trained with a conditional generative adversarial network to generate 3D posttreatment facial data based on the patient’s gender, age, and the changes of upper (ΔU1) and lower incisor position (ΔL1) as input. The accuracy was calculated with prediction error and mean absolute distances between real T2 (T2) and predicted T2 (PT2) near 6 perioral landmark regions, as well as percentage of prediction error less than 2 mm using test sets (n = 44). For qualitative evaluation, an online survey was conducted with experienced orthodontists as panels (n = 56). Overall, PT2 indicated similar 3D changes to the T2 face, with the most apparent changes simulated in the perioral regions. The mean prediction error was 1.2 ± 1.01 mm with 80.8% accuracy. More than 50% of the experienced orthodontists were unable to distinguish between real and predicted images. In this study, we proposed a valid 3D postorthodontic face prediction method by applying a deep learning algorithm trained with CBCT data sets.