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Background Artificial intelligence (AI)-based cephalometric tracing has emerged as a promising tool that reduces operator variability and offers standardized, rapid, and reproducible assessments. This study aimed to evaluate the reliability and accuracy of three cephalometric tracing methods: AI-based automatic digital tracing, semi-automatic digital tracing, and manual digital tracing. Materials & methods This study analyzed pre-treatment lateral cephalograms from 120 patients, comparing AI-based automatic digital tracing, semi-automatic digital tracing against manual digital tracing, which served as the control group. Angular and linear measurements were used as primary parameters, with 34 cephalometric measurements derived from 20 skeletal (SK) and 10 soft tissue (ST) landmarks. For parametric data, comparisons among the three methods were conducted using a repeated measures ANOVA test, while non-parametric data were analyzed using the Friedman test. A significance level of P  ≤ 0.05 was applied to all statistical analyses. Results In maxillary skeletal measurements, SNA (°) showed significant variation, with the Automatic method reporting slightly higher mean values ( P  < 0.001). Similarly, mandibular skeletal measurements revealed a small but significant difference in SNB (°) values ( P  = 0.002), while MP-SN (°) showed no notable differences ( P  = 0.118). For inter-maxillary measurements, ANB (°) displayed significant differences ( P  < 0.001), with the Automatic method reporting higher mean values. Vertical measurements, such as GO-GN to SN (°) and the Gonial Angle (°), also demonstrated significant variations ( P  < 0.001), with manual methods generally reporting higher values. In dental measurements, L1 to NB (°) differed significantly across methods ( P  = 0.022), and U1 to NA measurements showed both angular ( P  = 0.006) and linear ( P  < 0.001) significant differences, with Automatic methods tending to report lower medians. Lastly, soft tissue measurements, particularly the nasolabial angle (°), exhibited significant differences ( P  < 0.001), with the Manual method recording the highest mean values. Conclusions AI-based automatic tracing tended to overestimate certain skeletal values, while manual methods showed greater consistency. Dental measurements were largely comparable across methods. The semi-automatic approach provided a practical balance between accuracy and efficiency, indicating potential for clinical application with further refinement.

Background Artificial Intelligence has created a huge impact in different areas of dentistry. Automated cephalometric analysis is one of the major applications of artificial intelligence in the field of orthodontics. Various automated cephalometric software have been developed which utilizes artificial intelligence and claim to be reliable. The purpose of this study was to compare the linear and angular cephalometric measurements obtained from web-based fully automated Artificial Intelligence (AI) driven platform “WebCeph”™ with that from manual tracing and evaluate the validity and reliability of automated cephalometric measurements obtained from “WebCeph”™. Methods Thirty pre-treatment lateral cephalograms of patients were randomly selected. For manual tracing, digital images of same cephalograms were printed using compatible X-ray printer. After calibration, a total of 18 landmarks was plotted and 12 measurements (8 angular and 4 linear) were obtained using standard protocols. The digital images of each cephalogram were uploaded to “WebCeph”™ server. After image calibration, the automated cephalometric measurements obtained through AI digitization were downloaded for each image. Intraclass correlation coefficient (ICC) was used to determine agreement between the measurements obtained from two methods. ICC value < 0.75 was considered as poor to moderate agreement while an ICC value between 0.75 and 0.90 was considered as good agreement. Agreement was rated as excellent when ICC value > 0.90 was obtained. Results All the measurements had ICC value above 0.75. A higher ICC value > 0.9 was obtained for seven parameters i.e. ANB, FMA, IMPA/L1 to MP (°), LL to E-line, L1 to NB (mm), L1 to NB (°), S-N to Go-Gn whereas five parameters i.e. UL to E-line, U1 to NA (mm), SNA, SNB, U1 to NA (°) showed ICC value between 0.75 and 0.90. Conclusion A good agreement was found between the cephalometric measurements obtained from “WebCeph”™ and manual tracing.

Both standards are perfectly matched for term newborn babies. Because microcephaly cases were excluded from the InterGrowth samples, the distribution of head circumferences in the standard is appropriate for estimating specificity of a given cutoff.

The objective of this prospective study was to evaluate whether magnetic resonance imaging (MRI) is equivalent to lateral cephalometric radiographs (LCR, \"gold standard\") in cephalometric analysis. The applied MRI technique was optimized for short scanning time, high resolution, high contrast and geometric accuracy. Prior to orthodontic treatment, 20 patients (mean age ± SD, 13.95 years ± 5.34) received MRI and LCR. MRI datasets were postprocessed into lateral cephalograms. Cephalometric analysis was performed twice by two independent observers for both modalities with an interval of 4 weeks. Eight bilateral and 10 midsagittal landmarks were identified, and 24 widely used measurements (14 angles, 10 distances) were calculated. Statistical analysis was performed by using intraclass correlation coefficient (ICC), Bland-Altman analysis and two one-sided tests (TOST) within the predefined equivalence margin of ± 2°/mm. Geometric accuracy of the MRI technique was confirmed by phantom measurements. Mean intraobserver ICC were 0.977/0.975 for MRI and 0.975/0.961 for LCR. Average interobserver ICC were 0.980 for MRI and 0.929 for LCR. Bland-Altman analysis showed high levels of agreement between the two modalities, bias range (mean ± SD) was -0.66 to 0.61 mm (0.06 ± 0.44) for distances and -1.33 to 1.14° (0.06 ± 0.71) for angles. Except for the interincisal angle (p = 0.17) all measurements were statistically equivalent (p < 0.05). This study demonstrates feasibility of orthodontic treatment planning without radiation exposure based on MRI. High-resolution isotropic MRI datasets can be transformed into lateral cephalograms allowing reliable measurements as applied in orthodontic routine with high concordance to the corresponding measurements on LCR.

Background Accurate landmark localization is important for three-dimensional (3D) cephalometric analysis. Although deep learning has shown promising performance for 3D landmark localization, the high computational burden of processing volumetric data remains a challenge. The 2.5D networks have emerged to provide the good performance while mitigating computational and memory requirements in the medical domain. Therefore, we compared the performance of 2D, 2.5D and 3D network-based landmark localization. Methods We collected landmark datasets from the volumetric computed tomography (CT) scans of 40 patients. We implemented the 2D, 2.5D and 3D networks for 3D landmark localization. Additionally, we designed a global-to-local loss to mitigate foreground-background imbalance, and employed both soft and hard voting in a network ensemble to improve the robustness. We evaluated each network’s performance in terms of accuracy and computational load. Results The 2.5D network-based landmark localization achieved a mean radial error (MRE) of 1.19 0.65 mm and a successful detection rate (SDR) of 86.46% at 2 mm , with a favorable computational load. These results outperformed those of the 2D and 3D networks. Furthermore, using the global-to-local loss led to higher performance compared to using the global loss alone. Soft voting proved the most robust performance among voting methods for landmark localization. Conclusions Comprehensive experiments demonstrate that the 2.5D network offers an optimal trade-off between computational load and accuracy. These findings highlight the potential for more efficient and reliable 3D cephalometry under limited computational resources.

Purpose The main purpose of this study is to evaluate the facial morphology of obstructive sleep apnoea (OSA) individuals by using a handheld blue light three-dimensional (3D) scanner (HBL-3DS) in comparison to conventional lateral cephalometric radiography (LCR). Moreover, our research question is to explores the correlation between 3D facial and neck measurements with OSA indices, encompassing the hypoxic burden. Method This prospective cross-sectional study included forty-four adults with OSA. We compared three measurements between LCR and HBL-3DS images: modified facial profile angle (MFPA), nasolabial angle (NLA), and mandibular length (ML). Additionally, the 3D images of thirty-four participants with OSA indices were analysed for seventeen parameters, such as angles, ratios, and linear distances. Results This study revealed significant strong correlations ( p  < 0.001) between LCR and HBL-3DS in the measurements of MFPA ( r  = 0.675), NLA ( r  = 0.723), and ML ( r  = 0.675). However, no significant correlation was found between all predictors and the Apnoea-Hypopnea Index (AHI) or Oxygen Desaturation Index (ODI) in the 3D images of the thirty-four participants. Multivariate regression analysis demonstrated an independent negative correlation between mandibular width (MW) and nadir oxygen levels, while an independent positive correlation was observed between inner canthal width and the Rapid-Eye-Movement percentage (REM). Conclusions The study highlighted a significant association between LCR and HBL-3DS. HBL-3DS delivers precise 3D facial and neck measurements, presenting itself as a potentially cost-effective, radiation-free, and portable screening method for participants with OSA in clinical settings.

Background The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis. Methods Reconstructed lateral cephalograms (RLCs) from cone-beam computed tomography (CBCT) in 85 patients were selected. Computer-assisted manual analysis (Dolphin Imaging 11.9) and AI automatic analysis (Planmeca Romexis 6.2) were used to locate 19 landmarks and obtain 23 measurements. Mean radial error (MRE) and successful detection rate (SDR) values were calculated to assess the accuracy of automatic landmark digitization. Paired t tests and Bland‒Altman plots were used to compare the differences and consistencies in cephalometric measurements between manual and automatic analysis programs. Results The MRE for 19 cephalometric landmarks was 2.07 ± 1.35 mm with the automatic program. The average SDR within 1 mm, 2 mm, 2.5 mm, 3 and 4 mm were 18.82%, 58.58%, 71.70%, 82.04% and 91.39%, respectively. Soft tissue landmarks (1.54 ± 0.85 mm) had the most consistency, while dental landmarks (2.37 ± 1.55 mm) had the most variation. In total, 15 out of 23 measurements were within the clinically acceptable level of accuracy, 2 mm or 2°. The rates of consistency within the 95% limits of agreement were all above 90% for all measurement parameters. Conclusion Automatic analysis software collects cephalometric measurements almost effectively enough to be acceptable in clinical work. Nevertheless, automatic cephalometry is not capable of completely replacing manual tracing. Additional manual supervision and adjustment for automatic programs can increase accuracy and efficiency.

Objectives This study aimed to develop a cephalometric classification method based on multitask learning for eight diagnostic classifications. Methods This study was retrospective. A total of 3,310 lateral cephalograms were collected to construct a dataset. Eight clinical classifications were employed, including sagittal and vertical skeletal facial patterns, maxillary and mandibular anteroposterior positions, inclinations of upper and lower incisors, as well as their anteroposterior positions. The images were manually annotated for initially classification, which was verified by senior orthodontists. The data were randomly divided into training, validation, and test sets at a ratio of approximately 8:1:1. The multitask learning classification model was constructed based on the ResNeXt50_32 × 4d network and consisted of shared layers and task-specific layers. The performance of the model was evaluated using classification accuracy, precision, sensitivity, specificity and area under the curve (AUC). Results This model could perform eight clinical diagnostic classifications on cephalograms within an average of 0.0096 s. The accuracy of the six classifications was 0.8–0.9, and the accuracy of the two classifications was 0.75-0.8. The overall AUC values for each classification exceeded 0.9. Conclusions An automatic diagnostic classification model for lateral cephalograms was established based on multitask learning to achieve simultaneous classification of eight common clinical diagnostic items. The multitask learning model achieved better classification performance and reduced the computational costs, providing a novel perspective and reference for addressing such problems.

IntroductionCompensatory mouth breathing, caused by nasopharyngeal obstructive diseases, is the main cause of hyperdivergent mandibular retrognathia in children. Such deformities require effective growth guidance before pubertal growth peaks. The traditional mandibular advancement device, twin block (TB), can guide the forward development of the mandible. However, the side effect of increasing the vertical dimension of the lower facial third, worsens the facial profile of children with divergent growth trends. To solve this problem, a modified TB (LLTB) appliance was designed to control the vertical dimension by intruding incisors and inhibiting the elongation of posterior teeth during the advancement of the mandible, which could avoid the side effects of traditional appliances and effectively guide the growth of the mandible in a normal direction.Methods and analysisThe study was designed as a single-centre, single-blind, randomised, parallel controlled trial. We aim to enrol 60 children aged 9–14 years with hyperdivergent skeletal class II malocclusion, using a 1:1 allocation ratio. The participants were will be randomly assigned to receive either the TB or LLTB treatment. The primary outcome will be a change in the angle of the mandibular plane relative to the anterior cranial base. The secondary outcomes will include changes in the sagittal maxillomandibular relation, occlusal plane, facial height, morphology of the mandible and upper airway width. Safety endpoints will also be evaluated.Ethics and disseminationEthical approval was obtained from the ethics committee of Shanghai Stomatological Hospital. Both participants and their guardians will be fully informed of the study and sign an informed consent form before participating in the trial. The results will be publicly available in peer-reviewed scientific journals.Trial registration numberChiCTR2000035882.

Background Several appliances have been used for correction of developing skeletal Class II, including different myofunctional appliances as Twin block (TB)as well as the new pre-fabricated Myobrace (MB) appliance. However, the effects of these devices on the pharyngeal airways have not been compared in the literature. Thus, the aim of this study was to compare the effects of two Class II correction appliances; TB and MB on the sagittal pharyngeal airway dimension (SPAD), including the nasopharyngeal airway area (NPAA), the oropharyngeal airway area (OPAA), and the laryngopharyngeal airway area (LPAA). Methods This is a two parallel arms randomized comparative clinical trial. Twenty-six children of 9–12 years with Skeletal Class II malocclusion due to mandibular deficiency and normal maxillary growth as confirmed by lateral cephalometric X-ray readings (ANB angle > 4° and SNB angle < 78) and Cervical vertebral maturational index (CVMI) 1 or 2 were randomly assigned into two equal groups. Group I: TB, Group II: MB (prefabricated functional appliance, Myofunctional Research Co., Australia). Lateral cephalograms were taken for all patients in both groups before treatment (T1) and after treatment (6 months later) (T2). The primary aim was to assess pre and post treatment changes in the SPAD in each group, and compare between the two study groups. The secondary aim was to evaluate the sagittal skeletal measurements such as the SNA, SNB, ANB, Wits appraisal, as well as vertical skeletal measurements represented by the Frankfurt-mandibular plane angle (FMA) measured pre- and post-treatment. The independent samples t-test was used to compare the two study groups, and the mean difference and 95% confidence intervals (CI) were computed. The paired samples t-test was used to compare various parameters between T1 and T2 within each group. The cutoff for significance was p -value < 0.05. Data were analyzed using IBM SPSS for Windows (Version 26.0). Results By Comparing changes in airway measurements within each group, it was found that NPAA, OPAA, and LPAA increased significantly after treatment within each group of MB and TB. TB group showed significantly higher mean difference (T2-T1) in both NPAA and OPAA than MB group with 28.39 (± 56.75) and 40.46 (± 52.16) respectively. The increase in LPAA values was not statistically significant at (T2-T1) between both groups. Regarding skeletal changes, there was a significant increase in the SNB values between T1 and T2 within each group with 2.82 (± 3.32) for MB group and 3.79 (± 3.06) for TB group Moreover, there was a significant decrease in the ANB values between T1 and T2 within each group by 2.42 (± 2.70) for MB group and 3.06 (± 1.14) for TB group. Similarly, there was a significant decrease in the ANB values between T1 and T2 within each group by -2.13 (± 0.62) for MB group and − 2.46 (± 0.72) for TB group. No significant differences were found between both groups in SNA, SNB, ANB and Wits appraisal at p  = 0.06, p  = 0.45, p  = 0.43 and p  = 0.22 respectively. FMA did not show significant difference between T1 and T2 within each group, nor showed a significant mean difference between both groups at T2-T1. Conclusions TB was more effective than MB in improving the upper (NPAA) and middle (OPAA) airways, while no difference was found regarding the lower airway (LPAA). Both TB and MB reduced the severity of developing skeletal class II due to mandibular retrognathism by forward posturing of the mandible. Thus, patients with airway problems would benefit more from TB than MB.