Cephalometric Analysis Research Articles

Radiologic study of the distance between the maxillary central incisor root and the incisive canal according to skeletal malocclusion classification

The aim of this study was to evaluate the distance between the maxillary central incisor root and the incisive canal based on skeletal malocclusion classification and to analyze the morphology of the incisive canal using cone-beam computed tomography (CBCT). Skeletal malocclusion was categorized into Class I, II, and III using lateral cephalometric analysis. Measurements of the distance between the maxillary central incisor root and the incisive canal were taken at two levels: 2 mm (L1) and 4 mm (L2) superior to the labial cementoenamel junction of the maxillary central incisor. At L1, the distance was found to be closer in the Class II group compared to the Class I or Class III groups. Similarly, at L2, the Class II group exhibited a closer distance than the Class III group. Interestingly, females showed a closer distance compared to males at both L1 and L2. Further analysis revealed a significant gender difference in the Class I and III groups, but not in the Class II group. These findings emphasize the significance of evaluating the distance between the maxillary central incisor root and the incisive canal in patients with skeletal Class II malocclusion, regardless of gender.

The Reproducibility of Reference Landmarks in the External Acoustic Meatus (EAM) on Cone Beam Computed Tomography (CBCT) Images.

Objective: The aim of the present study is to identify a more reliable reference point in three-dimensional cephalometric analysis to replace the Porion point used in two-dimensional analysis, enhancing the accuracy of assessments. Methods: The methodology assessed potential alternative landmarks for three-dimensional cephalometric analysis. Utilizing a segmenting technique, anatomical landmarks were accurately pinpointed from the external acoustic meatus of 26 Cone Beam Computed Tomography (CBCT) scans. These landmarks were chosen for their clear and unambiguous detectability. To assess reproducibility, each landmark was replicated twice with a one-week interval by a master's student. Reproducibility was quantitatively evaluated by analyzing the absolute difference per axis. Results: Five possible candidate landmarks were identified: the most anterior, posterior, superior, and inferior points of the external acoustic meatus (EAM) and a notch delineating the epitympanic recess. The reproducibility of pinpointing these landmarks ranged from 0.56 mm to 2.2 mm. The absolute mean differences between measurements were 0.46 mm (SD 0.75) for the most anterior point, 0.36 mm (SD 0.44) for the most posterior point, 0.25 mm (SD 0.26) for the most superior point, 1.11 mm (SD 1.03) for the most inferior point, and 0.78 mm (SD 0.57) for the epitympanic notch. Conclusions: The most superior point of the EAM might successfully replace the Porion as an anatomical reference.

Outcome of patients with complete cleft lip and alveolus: 20-year follow-up.

Patients with cleft have functional and aesthetic impairment, and typically require several interventions as they grow. Long-term evaluation following a treatment protocol is essential, but such reports on patients with complete cleft lip and alveolus (CLA) are sparse in the literature. A retrospective review was conducted to all patients with complete CLA born between January 1995 and August 2002 and treated at our center. Patients who received continuous multidisciplinary team care until 20 years of age were included, and patients with cleft palate and syndromic abnormalities were excluded. Facial bone growth was evaluated using cephalometric analysis. Eighty-seven and 11 patients with unilateral and bilateral CLA (UCLA and BCLA) were included respectively. All patients received one-stage cheiloplasty with primary rhinoplasty. Revisional lip/nose surgery was performed in 21.8 and 27.3% during growing age, and in 51.7 and 72.7% after skeletal maturity. Orthognathic surgery was performed in 20.7 and 27.3%. Compared with UCLA patients, BCLA had larger number of operations (3.0 versus 3.7, p = 0.03) and higher chance of receiving alveolar bone grafting twice (1.1% versus 36.4%, p < 0.01). Patients with complete CLA had less hypoplastic maxilla, and received smaller number of operations than complete cleft lip and palate. Complete CLA is a less severe form of cleft, but the patients still require multiple interventions. This review revealed certain suboptimal results, and modifications have been made in the treatment protocol. Longitudinal follow-up and periodic assessment help to establish an ideal therapeutic strategy and improve overall cleft care.

Exploring the Practical Applications of Artificial Intelligence, Deep Learning, and Machine Learning in Maxillofacial Surgery: A Comprehensive Analysis of Published Works.

Artificial intelligence (AI), deep learning (DL), and machine learning (ML) are computer, machine, and engineering systems that mimic human intelligence to devise procedures. These technologies also provide opportunities to advance diagnostics and planning in human medicine and dentistry. The purpose of this literature review was to ascertain the applicability and significance of AI and to highlight its uses in maxillofacial surgery. Our primary inclusion criterion was an original paper written in English focusing on the use of AI, DL, or ML in maxillofacial surgery. The sources were PubMed, Scopus, and Web of Science, and the queries were made on the 31 December 2023. The search strings used were "artificial intelligence maxillofacial surgery", "machine learning maxillofacial surgery", and "deep learning maxillofacial surgery". Following the removal of duplicates, the remaining search results were screened by three independent operators to minimize the risk of bias. A total of 324 publications from 1992 to 2023 were finally selected. These were calculated according to the year of publication with a continuous increase (excluding 2012 and 2013) and R2 = 0.9295. Generally, in orthognathic dentistry and maxillofacial surgery, AI and ML have gained popularity over the past few decades. When we included the keywords "planning in maxillofacial surgery" and "planning in orthognathic surgery", the number significantly increased to 7535 publications. The first publication appeared in 1965, with an increasing trend (excluding 2014-2018), with an R2 value of 0.8642. These technologies have been found to be useful in diagnosis and treatment planning in head and neck surgical oncology, cosmetic and aesthetic surgery, and oral pathology. In orthognathic surgery, they have been utilized for diagnosis, treatment planning, assessment of treatment needs, and cephalometric analyses, among other applications. This review confirms that the current use of AI and ML in maxillofacial surgery is focused mainly on evaluating digital diagnostic methods, especially radiology, treatment plans, and postoperative results. However, as these technologies become integrated into maxillofacial surgery and robotic surgery in the head and neck region, it is expected that they will be gradually utilized to plan and comprehensively evaluate the success of maxillofacial surgeries.

A questionnaire study regarding knowledge, attitude and usage of artificial intelligence and machine learning by the orthodontic fraternity of Northern India

AimThe aim of the questionnaire study was to determine the knowledge, attitude, and perception of orthodontists regarding the role of artificial intelligence in dentistry in general and orthodontics specifically, and to determine the use of artificial intelligence by the orthodontist. MethodsThis cross-sectional study was done among the orthodontists of Northern India (clinicians, academicians, and postgraduates) through a web-based electronic survey using Google Forms. The study was designed to obtain information about AI and its basic usage in daily life, in dentistry, and in orthodontics from the participants. The options given were set specifically according to the Likert scale to maintain the correct format. The questionnaire was validated by one AI expert and one orthodontic expert, followed by pretesting in a smaller group of 25 orthodontists 2 weeks before circulation. A total of 100 orthodontists and postgraduate students responded to the pretested online questionnaire link for 31 questions in four sections sent via social media websites in a period of 3 months. ResultsThe majority of the participants believe that AI could be useful in diagnosis and treatment planning and could revolutionize dentistry in general. 84 % of the orthodontic academicians and clinicians, including PG students, consider AI a useful tool for boosting performance and delivering quality care in orthodontics, and 72 % see AI as a partner rather than a competitor in the foreseeable future of dentistry. 90 % of the participants believe that the incorporation of AI into CBCT analysis can be a valuable addition to diagnosis and treatment planning. 86 % of total participants agree that AI can be helpful in decision-making for orthognathic surgery, and 84 % find AI useful for bone age assessment. ConclusionsIt was observed that academicians are more aware of AI terminologies and usage as compared to PG students and clinicians. There is a consensus that AI is a useful tool for diagnosis and treatment planning, boosting performance and quality care in orthodontics. In spite of these facts, 62.5 % of clinicians and 40 % of PG students are still not using AI for cephalometric analysis (p = 0.033).

Craniomaxillofacial landmarks detection in CT scans with limited labeled data via semi-supervised learning

BackgroundThree-dimensional cephalometric analysis is crucial in craniomaxillofacial assessment, with landmarks detection in craniomaxillofacial (CMF) CT scans being a key component. However, creating robust deep learning models for this task typically requires extensive CMF CT datasets annotated by experienced medical professionals, a process that is time-consuming and labor-intensive. Conversely, acquiring large volume of unlabeled CMF CT data is relatively straightforward. Thus, semi-supervised learning (SSL), leveraging limited labeled data supplemented by sufficient unlabeled dataset, could be a viable solution to this challenge. MethodWe developed an SSL model, named CephaloMatch, based on a strong-weak perturbation consistency framework. The proposed SSL model incorporates a head position rectification technique through coarse detection to enhance consistency between labeled and unlabeled datasets and a multilayers perturbation method which is employed to expand the perturbation space. The proposed SSL model was assessed using 362 CMF CT scans, divided into a training set (60 scans), a validation set (14 scans), and an unlabeled set (288 scans). ResultThe proposed SSL model attained a detection error of 1.60 ± 0.87 mm, significantly surpassing the performance of conventional fully supervised learning model (1.94 ± 1.12 mm). Notably, the proposed SSL model achieved equivalent detection accuracy (1.91 ± 1.00 mm) with only half the labeled dataset, compared to the fully supervised learning model. ConclusionsThe proposed SSL model demonstrated exceptional performance in landmarks detection using a limited labeled CMF CT dataset, significantly reducing the workload of medical professionals and enhances the accuracy of 3D cephalometric analysis.

Establishment of Cephalometric Hard Tissue Profile among Bengali and Caucasian Populations for Orthognathic Surgery

ABSTRACT The study aimed to compare cephalometric hard tissue profile values and analysis between Bengali and Caucasian populations. The study included 100 individuals (50 males, 50 females) aged 18–35. Subjects having class I molar occlusions and well-balanced facial profiles were included in the study. Lateral cephalograms were taken with consent, and a prestructured proforma recorded relevant information and cephalometric measurements. Our study found increased posterior cranial base length in Bengali males and increased anterior cranial base length in both sexes. Bengali males had a less convex profile, while females had a more convex profile compared to Caucasians with similar mandibular features. Increased maxillary and mandibular incisor proclination was observed. Our findings indicate the necessity for separate norms for orthognathic surgery in normal ethnic Bengali adults.

Comparison of conventional and cone beam computed tomography synthetized cephalograms in evaluating different skeletal patterns

Abstract Objectives To compare conventional and cone beam computed tomography (CBCT) synthetized cephalograms in evaluating different skeletal patterns. Patients and methods This cross-sectional study was carried out on 30 CBCT scans and lateral cephalograms of Egyptian orthodontic patients of both sexes with different skeletal malocclusion patterns. Their ages ranging from 18 to 25 years old before orthodontic treatment. The conventional radiographs were called LCR images, and the cephalograms dragged from CBCT were called CBCT images. All patients were subjected to cephalometric analysis. Results There was a highly statistically significant difference between both groups in angular measurements SNA°, SNB°, and FMP°. Also, there were statistically significant differences between both groups in SN/MP°, U1/FHP°, and L1/MP°. However, there were no statistically significant differences between both groups regarding ANB°, Y-axis°, and Inter Incisal Angle. Conclusions CBCT-synthesized cephalograms demonstrated more accurate dimensional measurements than conventional lateral cephalograms. CBCT-synthesized cephalograms could be confidently used for the evaluation of different skeletal malocclusion. CBCT could successfully replace conventional cephalograms for orthodontic diagnosis and treatment planning.

Assessing the Efficacy of Early versus Late Orthodontic Intervention in the Management of Class II Malocclusion: A Comparative Analysis

ABSTRACT Background: Class II malocclusion is a common orthodontic issue characterized by the excessive protrusion of the upper teeth relative to the lower teeth. Early intervention during mixed dentition has been advocated to guide skeletal growth and potentially reduce the severity of malocclusion. Materials and Methods: A comparative analysis was conducted to evaluate the efficacy of early (before the pubertal growth spurt) versus late (after the pubertal growth spurt) orthodontic intervention in managing Class II malocclusion. A cohort of 100 patients diagnosed with Class II malocclusion was divided into two groups: early intervention group (n = 50) and late intervention group (n = 50). Treatment outcomes including changes in overjet, molar relationship, and cephalometric measurements were assessed before and after orthodontic treatment. Results: In the early intervention group, the mean reduction in overjet was 5.2 mm (SD = 1.3), while in the late intervention group, it was 3.8 mm (SD = 1.6). The improvement in molar relationship was greater in the early intervention group (mean change = 4.5 mm, SD = 1.1) compared to the late intervention group (mean change = 3.1 mm, SD = 1.4). Cephalometric analysis revealed significant differences in skeletal and dental parameters between the two groups, with greater improvements observed in the early intervention group. Conclusion: Early orthodontic intervention shows superior efficacy in managing Class II malocclusion compared to late intervention. Early treatment leads to greater reductions in overjet, improvements in molar relationship, and favorable changes in skeletal and dental parameters. These findings underscore the importance of timely orthodontic intervention, particularly during the mixed dentition stage, for optimal management of Class II malocclusion.

Cephalometric Evaluation of Children with Short Stature of Genetic Etiology: A Review.

Introduction: A plethora of biological molecules regulate chondrogenesis in the epiphyseal growth plate. Disruptions of the quantity and function of these molecules can manifest clinically as stature abnormalities of various etiologies. Traditionally, the growth hormone/insulin-like growth factor 1 (IGF1) axis represents the etiological centre of final stature attainment. Of note, little is known about the molecular events that dominate the growth of the craniofacial complex and its correlation with somatic stature. Aim: Given the paucity of relevant data, this review discusses available information regarding potential applications of lateral cephalometric radiography as a potential clinical indicator of genetic short stature in children. Materials and Methods: A literature search was conducted in the PubMed electronic database using the keywords: cephalometric analysis and short stature; cephalometric analysis and achondroplasia; cephalometric analysis and hypochondroplasia; cephalometric analysis and skeletal abnormalities; cephalometr* and SHOX; cephalometr* and CNP; cephalometr* and ACAN; cephalometr* and CNVs; cephalometr* and IHH; cephalometr* and FGFR3; cephalometr* and Noonan syndrome; cephalometr* and "Turner syndrome"; cephalometr* and achondroplasia. Results: In individuals with genetic syndromes causing short stature, linear growth of the craniofacial complex is confined, following the pattern of somatic short stature regardless of its aetiology. The angular and linear cephalometric measurements differ from the measurements of the average normal individuals and are suggestive of a posterior placement of the jaws and a vertical growth pattern of the face. Conclusions: The greater part of the existing literature regarding cephalometric measurements in short-statured children with genetic syndromes provides qualitative data. Furthermore, cephalometric data for individuals affected with specific rare genetic conditions causing short stature should be the focus of future studies. These quantitative data are required to potentially establish cut-off values for reference for genetic testing based on craniofacial phenotypes.

Genetic Insights into Skeletal Malocclusion: The Role of the FBN3 rs7351083 SNP in the Romanian Population.

Background and Objectives: irregularities in the growth and development of the jawbones can lead to misalignments of maxillary and mandibular structures, a complex condition known as skeletal malocclusion, one of the most common oral health problems. Skeletal malocclusions, particularly Class II and Class III, can significantly affect facial appearance, chewing efficiency, speech, and overall oral health, often requiring orthodontic treatment or surgery to correct. These dentofacial anomalies are influenced by genetic and environmental factors and exhibit diverse phenotypic expressions. Materials and Methods: in this study, we investigated the correlation between the rs7351083 SNP of the FBN3 gene that encodes a member of the fibrillin protein family and malocclusion risk in a group of 57 patients from Romania. Results: the results shed light on the relationship between the selected genetic marker and the investigated dentofacial disorder, revealing a positive association between the reference allele (A) and Class II and that the alternate allele (G) is associated with Class III. Conclusions: cephalometric analysis revealed no significant differences among genotypes, suggesting that while genetic factors are implicated in malocclusion, they may not directly affect cephalometric parameters or that the sample size was too small to detect these differences. The discovery of an A > T transversion in one individual with a Class II deformity underscores the genetic diversity within the population and the necessity of comprehensive genotyping to uncover rare genetic variants that might influence craniofacial development and the risk of malocclusion. This study highlights the need for larger studies to confirm these preliminary associations.

Comparison of Three Commercially Available, AI-Driven Cephalometric Analysis Tools in Orthodontics.

Background: Cephalometric analysis (CA) is an indispensable diagnostic tool in orthodontics for treatment planning and outcome assessment. Manual CA is time-consuming and prone to variability. Methods: This study aims to compare the accuracy and repeatability of CA results among three commercial AI-driven programs: CephX, WebCeph, and AudaxCeph. This study involved a retrospective analysis of lateral cephalograms from a single orthodontic center. Automated CA was performed using the AI programs, focusing on common parameters defined by Downs, Ricketts, and Steiner. Repeatability was tested through 50 randomly reanalyzed cases by each software. Statistical analyses included intraclass correlation coefficients (ICC3) for agreement and the Friedman test for concordance. Results: One hundred twenty-four cephalograms were analyzed. High agreement between the AI systems was noted for most parameters (ICC3 > 0.9). Notable differences were found in the measurements of angle convexity and the occlusal plane, where discrepancies suggested different methodologies among the programs. Some analyses presented high variability in the results, indicating errors. Repeatability analysis revealed perfect agreement within each program. Conclusions: AI-driven cephalometric analysis tools demonstrate a high potential for reliable and efficient orthodontic assessments, with substantial agreement in repeated analyses. Despite this, the observed discrepancies and high variability in part of analyses underscore the need for standardization across AI platforms and the critical evaluation of automated results by clinicians, particularly in parameters with significant treatment implications.

Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset.

Cephalometric analysis is critically important and common procedure prior to orthodontic treatment and orthognathic surgery. Recently, deep learning approaches have been proposed for automatic 3D cephalometric analysis based on landmarking from CBCT scans. However, these approaches have relied on uniform datasets from a single center or imaging device but without considering patient ethnicity. In addition, previous works have considered a limited number of clinically relevant cephalometric landmarks and the approaches were computationally infeasible, both impairing integration into clinical workflow. Here our aim is to analyze the clinical applicability of a light-weight deep learning neural network for fast localization of 46 clinically significant cephalometric landmarks with multi-center, multi-ethnic, and multi-device data consisting of 309 CBCT scans from Finnish and Thai patients. The localization performance of our approach resulted in the mean distance of 1.99 ± 1.55 mm for the Finnish cohort and 1.96 ± 1.25 mm for the Thai cohort. This performance turned out to be clinically significant i.e., ≤ 2 mm with 61.7% and 64.3% of the landmarks with Finnish and Thai cohorts, respectively. Furthermore, the estimated landmarks were used to measure cephalometric characteristics successfully i.e., with ≤ 2 mm or ≤ 2° error, on 85.9% of the Finnish and 74.4% of the Thai cases. Between the two patient cohorts, 33 of the landmarks and all cephalometric characteristics had no statistically significant difference (p < 0.05) measured by the Mann-Whitney U test with Benjamini-Hochberg correction. Moreover, our method is found to be computationally light, i.e., providing the predictions with the mean duration of 0.77 s and 2.27 s with single machine GPU and CPU computing, respectively. Our findings advocate for the inclusion of this method into clinical settings based on its technical feasibility and robustness across varied clinical datasets.

A Comparative Study: Photogrammetry and Cephalometric Analysis for Orthodontic Treatment Planning

Introduction: Cephalometric analysis constitutes the benchmark for diagnosing craniofacial morphology in orthodontic practice. However, the increasing demand for aesthetic standards has clinicians questioning whether ideal cephalometric measurements are synonymous with facial aesthetics since soft tissues do not necessarily follow the underlying skeletal profile. In addition, rising concerns about radiation exposure have necessitated a non-radiographic, non-invasive and cost-effective alternative. Facial photography has been an integral part of orthodontic treatment planning but usually as an adjunct to record keeping. However, recent studies have proven the reliability of using photographs for soft-tissue analysis. It provides an easily reproducible, economical and low technique-sensitive procedure to evaluate cranial morphology, especially for epidemiologic research. The primary aim of this study is to determine if photographic analysis could be utilised with precision for orthodontic diagnosis. Materials and Methods: The study was conducted in the Department of Orthodontics, J. S. S. Dental College and Hospital, Mysuru. This study selected 100 patients aged 12–28 years who had reported to the department for orthodontic treatment. Standardised profile photographs and lateral cephalograms were obtained from the patients as part of the records required. Traditional cephalometric (angular and linear) measurements and corresponding photographic ones used for sagittal and vertical assessment were analysed to draw inferences. Results: The reliability of the photographic technique was proven with intraclass correlation coefficients above 0.79. A highly significant correlation (P &lt; 0.001) was found between all parameters. N-Sn-Pog/N’-Sn-Pog’ (0.946), SN-MP/TrN’-MP” (0.844) and FMA/FMA’ showed the highest coefficients of correlation. The photographic variable that best explained the variability of its analogous cephalometric measurement was the N-SN-Pog angle (r 2 = 0.895). Conclusion: The photographic method has proven to be repeatable and reproducible. With a homogeneous protocol, the photographic method is a practical alternative to lateral cephalograms, especially in cases that require a non-invasive, low-cost option.

Evaluation of the soft tissue facial profile in different skeletal malocclusions in relation to age

BackgroundThe aim of the study was to assess the thickness of the soft tissue facial profile (STFP) in relation to the skeletal malocclusion, age and gender.MethodsAll patients, aged 7–35 years, who were seeking orthodontic treatment at the Department of Orthodontics, Medical University of Warsaw between 2019 and 22 were included in the study. All patients had lateral head radiographs taken before the treatment. The cephalometric analysis was performed including the STFP analysis. The patients were allocated to one of six groups based on age and skeletal relations (ANB angle). The minimum number of patients in each group was 60 with equal gender distribution. The STFP analysis included ten linear measurements.ResultsA total of 300 patients were included in the study and allocated to five groups. Group 6 (growing patients with skeletal Class III malocclusion) was not included in the study as it failed to achieve the assumed group size. There were significant differences in the thickness of the STFP in relation to the skeletal malocclusions. Adults with skeletal Class III malocclusion had significantly thicker subnasal soft tissues compared to patients with skeletal Class I and Class II malocclusions. The thickness of the lower lip in patients with Class II skeletal malocclusion was significantly bigger compared to the other groups. Children and adolescents with Class II malocclusions had thicker lower lip in comparison to the group with Class I malocclusion. The majority of the STFP measurements were significantly smaller in children and adolescents compared to adults. The thickness of the STFP in males was significantly bigger in all age groups compared to females.ConclusionsThe thickness of facial soft tissues depends on the patient’s age and gender. The degree of compensation of the skeletal malocclusion in the STFP may be a decisive factor during orthodontic treatment planning regarding a surgical approach or a camouflage treatment of skeletal defects.

Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis.

This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and three-dimensional (3D) cone-beam computed tomographic (CBCT) images. An electronic search was conducted in the following databases: PubMed, Web of Science, Embase, and grey literature with search timeline extending up to January 2024. Studies that employed AI for 2D or 3D cephalometric landmark detection were included. The selection of studies, data extraction, and quality assessment of the included studies were performed independently by two reviewers. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A meta-analysis was conducted to evaluate the accuracy of the 2D landmarks identification based on both mean radial error and standard error. Following the removal of duplicates, title and abstract screening, and full-text reading, 34 publications were selected. Amongst these, 27 studies evaluated the accuracy of AI-driven automated landmarking on 2D lateral cephalograms, while 7 studies involved 3D-CBCT images. A meta-analysis, based on the success detection rate of landmark placement on 2D images, revealed that the error was below the clinically acceptable threshold of 2mm (1.39mm; 95% confidence interval: 0.85-1.92mm). For 3D images, meta-analysis could not be conducted due to significant heterogeneity amongst the study designs. However, qualitative synthesis indicated that the mean error of landmark detection on 3D images ranged from 1.0 to 5.8mm. Both automated 2D and 3D landmarking proved to be time-efficient, taking less than 1min. Most studies exhibited a high risk of bias in data selection (n = 27) and reference standard (n = 29). The performance of AI-driven cephalometric landmark detection on both 2D cephalograms and 3D-CBCT images showed potential in terms of accuracy and time efficiency. However, the generalizability and robustness of these AI systems could benefit from further improvement. PROSPERO: CRD42022328800.

Evaluation of Mandibular Projection in Class II Division 2 Subjects Following Orthodontic Treatment Using Clear Aligners.

The purpose of this study was to evaluate the mandibular growth and/or projection following maxillary incisor proclination, overbite correction, and maxillary dentoalveolar expansion without the use of any class II mechanics, in growing class II division 2 patients treated with clear aligners. Before and after treatment cone-beam computed tomographic (CBCT) generated lateral and posteroanterior cephalograms of thirty-two patients with skeletal class II division 2, 16 in the treatment group and 16 in the untreated group, were reviewed to evaluate treatment-related changes. Upper incisors were proclined and protruded, as well as upper arch expansion and overbite correction were performed as part of their regular treatment. Cephalometric analysis was performed to evaluate skeletal and dental changes. Unpaired statistical t-tests were performed to determine if significant skeletal class II correction was achieved in the treatment group. In the treatment group, after treatment, the upper incisors became more proclined and protruded, and the inter-molar width increased while the overbite was reduced compared to the control group. An increase in skeletal mandibular growth and forward projection was also observed, thus contributing to an improvement of the sagittal skeletal relationship as evidenced by ANB and Wits values compared to the control group. A combination of upper incisor proclination, correction of deep overbite, and maxillary dentoalveolar expansion using clear aligners appears to contribute to an improvement of the skeletal class II relationship in growing patients with class II division 2. This study shows that unlocking the mandible by correcting a deep overbite, proclining upper incisors, and expanding the upper arch in growing class II division 2 patients can improve skeletal class II using clear aligners. How to cite this article: Mirzasoleiman P, El-Bialy T, Wiltshire WA, et al. Evaluation of Mandibular Projection in Class II Division 2 Subjects Following Orthodontic Treatment Using Clear Aligners. J Contemp Dent Pract 2024;25(4):295-302.

Cephalometric analysis with magnetic resonance imaging in patients with obstructive sleep apnea syndrome

Objectives: The study aimed to assess soft tissues and bony structures in patients with snoring and obstructive sleep apnea syndrome (OSAS) by magnetic resonance imaging (MRI) cephalometry and to investigate the correlation between the degree of OSAS with cephalometric measures. Patients and Methods: The prospective study included 137 adult patients (86 males, 51 females; mean age: 44.2±11.8; range 21 to 70 years) admitted with complaints of snoring or witnessed apnea between December 2020 and June 2023. Polysomnography and MRI cephalometry were performed for each patient. According to the polysomnography results, patients were assigned to one of the following groups: simple snorers [apnea-hypopnea index (AHI) &lt;5; n=25], mild OSAS (AHI between 5 and 15; n=37), moderate OSAS (AHI between 15 and 30; n=36), and severe OSAS (AHI &gt;30; n=39). The SNA (sella, nasion-point A), SNB (sella, nasion-point B), submental fat thickness, retropalatal and retro glossal distance, tongue, uvula, basion, and hyoid-related cephalometric measurements were evaluated with MRI cephalometry. Results: The H-A (hyoid-point A) values were found to be higher in the severe OSAS patients than in other groups (p&lt;0.001, p&lt;0.001 and p&lt;0.05, respectively). The PNS-UT (posterior nasal spine-uvula tip) values of the patients in the severe OSAS group were found to be higher than patients in both the snoring and moderate OSAS groups (p&lt;0.001). The SNB values of the patients in the mild OSAS group were found to be higher than the patients in the snoring group (p&lt;0.05). In the parameters of UW (uvula thickness), PAS-RG (posterior airway space-retroglossal distance), and H-B (hyoid-point B), statistically meaningful differences were found only between the simple snorers group and the group with severe OSAS. Conclusion: In OSAS patients, some important distances, angles, and lengths can be evaluated by avoiding ionizing radiation with MRI cephalometry.

The Future of Orthodontics: Deep Learning Technologies.

Deep learning has emerged as a revolutionary technical advancement in modern orthodontics, offering novel methods for diagnosis, treatment planning, and outcome prediction. Over the past 25 years, the field of dentistry has widely adopted information technology (IT), resulting in several benefits, including decreased expenses, increased efficiency, decreased need for human expertise, and reduced errors. The transition from preset rules to learning from real-world examples, particularly machine learning (ML) and artificial intelligence (AI), has greatly benefited the organization, analysis, and storage of medical data. Deep learning, a type of AI, enables robots to mimic human neural networks, allowing them to learn and make decisions independently without the need for explicit programming. Its ability to automate cephalometric analysis and enhance diagnosis through 3D imaging has revolutionized orthodontic operations. Deep learning models have the potential to significantly improve treatment outcomes and reduce human errors by accurately identifying anatomical characteristics on radiographs, thereby expediting analytical processes. Additionally, the use of 3D imaging technologies such as cone-beam computed tomography (CBCT) can facilitate precise treatment planning, allowing for comprehensive examinations of craniofacial architecture, tooth movements, and airway dimensions. In today's era of personalized medicine, deep learning's ability to customize treatments for individual patients has propelled the field of orthodontics forward tremendously. However, it is essential to address issues related to data privacy, model interpretability, and ethical considerations before orthodontic practices can use deep learning in an ethical and responsible manner. Modern orthodontics is evolving, thanks to the ability of deep learning to deliver more accurate, effective, and personalized orthodontic treatments, improving patient care as technology develops.

Artificial Intelligence and its Awareness and Utilization among Dental Students and Private Dental Practitioners at Alkharj, Saudi Arabia.

Artificial intelligence (AI) is commonly used in the modern day medical system for medical and dental imaging diagnostics, decision support, precision, hospital monitoring, robotic assistants, and so on. All branches of dentistry have a role of AI, like endodontics, cancer diagnosis, and cephalometric analysis. With the advancing technology, dental professionals need to upgrade themselves. To assess awareness and attitude of dental students and dental practitioners in Alkharj toward AI. A total of 100 dental students from a teaching institute and 100 private dental practitioners participated in the study. A closed-ended questionnaire was used containing 14 questions related to awareness and attitude toward AI. Participation was voluntary. 33% of study participants were aware of the working principle of AI; 68% of study subjects are aware of uses of AI in the dental field. 87% thinks AI helps in radiological diagnosis; 56.5% thinks AI helps in cancer detection. Awareness about AI among study participants was less than 50%. The overall attitude of dental professionals was positive.