Correlation Between Cephalometric Values and Soft Tissue Profile in Class I and Class II Adult Patients based on Vertical Patterns.

Objective: To compare soft tissue profile variations between Class I and Class II adult patients due to three vertical skeletal facial patterns (normodivergent, hypodivergent and hyperdivergent) and determine which skeletal variation has the most significant impact on soft tissue profile. Methods: Retrospective soft tissue profile analysis was performed on lateral cephalograms of 131 adult patients. The analysis was divided into two categories correlated with subnasal and general soft tissue profiles. The sample was divided based on two sagittal skeletal patterns (Class I and II) and three vertical groups. In addition, comparisons were made between males and females. Viewbox 4 was used for the analysis. Descriptive, comparative, and correlation statistics were performed using SPSS software. Results: Statistically significant inter-gender differences were found at the subnasal profile level, but not at the general profile level. No significant differences were observed when comparing subnasal profiles for the sagittal groups. However, significant differences were observed at the level of the general profile, especially at the level of Z-angle, lower lip, and chin prominence. In the vertical groups, hyperdivergent facial patterns had significant differences at the level of subnasal and general profiles compared with other vertical facial patterns. Conclusion: Females had more convex subnasal profiles than males. Hyperdivergent facial patterns had an impact on both general and subnasal soft tissue profiles. The sagittal dimension affected only the general soft tissue profile. Therefore, changes in the vertical dimension had the greatest impact on facial esthetics.

Evaluation of mandibular growth in a prepubertal Class I and Class II population: a longitudinal analysis.

OBJECTIVE: This study aims to identify the presence, timing, and magnitude of a prepubertal mandibular growth spurt in a Class I and Class II population. METHODS: From the Burlington and Iowa Growth study of the AAOF Craniofacial Growth Legacy Collection, 83 Class I subjects (37 females and 46 males) and 32 Class II subjects (18 males and 14 females) were identified, as having at least seven consecutive annual lateral cephalograms taken from 5 to 11 years of age. Only subjects with a normodivergent facial pattern were considered. A customized cephalometric analysis was performed, and total mandibular length, defined as the distance between Condylion (Co) and Gnathion (Gn), was calculated. RESULTS: Overall, a significant early peak of mandibular growth was present in all the subjects analysed both in Class I (4.69 mm for males and 4.18 mm for females; P < .05) and in Class II (5.85 mm for males and 4.05 mm for females; P < .05). No differences between males and females were found for the timing of this peak (7 years for Class I and Class II females and 7 years for Class I and 6.5 years for Class II males). In males, a significantly larger peak was observed in Class II than Class I subjects (P = .007). LIMITATIONS: The main limitations of this study were the impossibility of using a suitable growth indicator to identify the timing of the early mandibular growth peak and the limited Class II records retrievable. CONCLUSION: This investigation suggests that a prepubertal mandibular growth peak is consistently present in both Class I and Class II males and females of clinically significant magnitude. Despite that, chronological age confirms to be unsuitable to identify this peak.

A Comparative Analysis of Skeletal and Dental Parameters in Bilateral Cleft Lip and Palate vs. Non-bilateral Cleft Lip and Palate Patients in the Central Indian Population: A NemoCeph Study.

Introduction Orthodontic diagnosis and treatment planning encounter distinctive complexities when dealing with cleft lip and palate anomalies. This research endeavors to thoroughly examine skeletal and dental characteristics through cephalometric analyses among individuals with bilateral cleft lip and palate (BCLP) within the central Indian population. Due to anatomical variations and growth constraints, traditional cephalometric mean values derived from standard population studies are often inadequate for these cases. Advanced technology, such as NemoCeph (Nemotech, Madrid, Spain) software, enhances measurement accuracy. Methods Fifty patients, including 25 with BCLP and 25 without BCLP, aged 10 to 18, were selected for this cross-sectional study. Lateral cephalograms were traced and analyzed using NemoCeph software. Skeletal and dental parameters were measured, and a comparison was made between BCLP patients and the general population. Statistical analysis was conducted using the Student's unpaired t-test. Both SPSS Statistics Version 24.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism Version 7.0 (GraphPad Software, San Diego, CA, USA) were used for data analysis. Results The investigation revealed significant disparities across several parameters, including sella-nasion-A point angle (SNA), sella-nasion-B point angle (SNB), A point-nasion-B point angle (ANB), the inter-incisal angle (the angle between the long axes of the upper and lower incisors), and UP 1 to A-pog (a specific vertical measurement between anatomical markers labeled "upper 1" and "A point to pogonion"), with associated p-values for the skeletal and dental parameters of 0.310, 0.259, 0.195, 0.0001, and 0.0001, respectively. A comparison between manual tracing and digital methods indicated a reduction in errors and an improvement in measurement precision. Notably, patients diagnosed with BCLP exhibited distinctive skeletal and dental traits, highlighting the necessity for tailored treatment approaches. Conclusion This study emphasizes the importance of personalized cephalometric evaluations for patients with BCLP. Standard mean values may not be applicable due to unique anatomical considerations in these cases. Advanced technology and patient-specific assessments are crucial for accurate diagnosis, treatment planning, and orthognathic procedures in individuals with cleft lip and palate conditions. Embracing digital tools and tailored approaches can enhance patient care quality and lead to better clinical outcomes.

Cephalometric analysis of patients with beta thalassemia receiving fetal hemoglobin induction therapy.

Objective: We aimed to determine the effects of fetal hemoglobin induction therapy in restricting or even reversing the cephalometric changes associated with beta thalassemia. Materials and methods: In this comparative observational study, 90 participants were equally divided into three groups: a control group; patients with thalassemia major receiving blood transfusion (BT group); and patients receiving induction therapy (i.e., hydroxyl urea (5-10 mg/kg/day) or as much as 20 mg/kg/day) and thalidomide (2-10 mg/kg/day) along with blood transfusion (IT group). All patients underwent history taking and examination, photographic assessment, and radiographic evaluation with a lateral cephalogram. One-way ANOVA followed by post-hoc Tukey test was used to determine differences among groups. Results: The IT group differed significantly from the BT group in all photographic and skull table parameters, and most cephalometric parameters, such as facial angle (p ≤ 0.001), middle and lower facial heights (p ≤ 0.001), and inter-incisal angle (p = 0.036); the mean values in the IT group were similar to those in the control group. In-addition, dental and soft tissue measurements significantly differed among groups. For most parameters, the mean difference indicated higher values in the BT group. Conclusion: Induction therapy appeared to improve the facial angles, heights, and inter-incisal angles, whereas a class II skeletal pattern was observed in the transfusion only group. These findings suggest that fetal hemoglobin induction therapy might have restricted some of the cephalometric changes in patients with beta thalassemia.

Accuracy and tracing time of cephalometric analyses on a tablet or desktop computer : A prospective study.

BACKGROUND: This prospective study aimed to evaluate the influence of the computer type (tablet or desktop) on accuracy and tracing time of cephalometric analyses. METHODS: Dental students used a web-based application specifically developed for this purpose to perform cephalometric analyses on tablet and desktop computers. Landmark locations and timestamps were exported to measure the accuracy, successful detection rate and tracing time. Reference landmarks were established by six experienced orthodontists. Statistical analysis included reliability assessment, descriptive statistics, and linear mixed effect models. RESULTS: Over a period of 8 semesters a total of 277 cephalometric analyses by 161 students were included. The interrater reliability of the orthodontists establishing the reference coordinates was excellent (ICC > 0.9). For the students, the mean landmark deviation was 2.05 mm and the successful detection rate for the clinically acceptable threshold of 2 mm suggested in the literature was 68.6%, with large variations among landmarks. No effect of the computer type on accuracy and tracing time of the cephalometric analyses could be found. CONCLUSION: The use of tablet computers for cephalometric analyses can be recommended.

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review.

The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.

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

PURPOSE: 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. MATERIALS AND METHODS: 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). RESULTS: 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. CONCLUSIONS: 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. CLINICAL SIGNIFICANCE: 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.

Association Between Gender, Age, and Skeletal Class With Mandibular Condyle Morphology: A Retrospective Study.

OBJECTIVES: This study aimed to examine the impact of demographic variables on mandibular condyle morphology, a critical factor in orthodontic treatment and maxillofacial surgery. The investigation focuses on the relationship between gender, age, and skeletal class with the morphological dimensions of the condyle, utilizing panoramic radiography as a diagnostic tool. METHODOLOGY: A retrospective analysis was conducted on 150 panoramic radiographs from individuals stratified into six groups according to gender and skeletal class. Skeletal classes were determined using Steiner and McNamara cephalometry. The Kodak Carestream software (Rochester, NY: Carestream Health) was employed to measure condylar height, width, and morphology. Statistical evaluations included ANOVA, correlation assessments, and multivariate binary logistic regression to discern the differences and associations among the variables studied. RESULTS: The findings revealed notable differences in condylar dimensions between genders across different skeletal classes, with males typically presenting larger condylar dimensions than females. The data also showed a moderate positive correlation between condyle height and width. Round-shaped condyles were the most common form found, with significant gender differences observed in certain skeletal classes. Additionally, logistic regression analysis identified significant associations between gender, age, and condylar width and shape. CONCLUSIONS: The study concludes that demographic factors, such as gender and age, significantly affect mandibular condyle morphology. These factors should be carefully considered in clinical evaluations using panoramic radiography to enhance the precision of diagnoses and the effectiveness of subsequent orthodontic and maxillofacial treatments. The results provide valuable insights for healthcare professionals in regions where more advanced imaging techniques may not be readily available.

Reproducibility of Computerized Cephalometric Analysis Software Compared with Conventional Manual Tracing for Analyzing Skeletal Stability After Orthognathic Surgery.

Objective: This study aimed to compare the difference between analyzing skeletal stability after orthognathic surgery by lateral cephalogram measurement created from Dolphin software (version 11.95) compared with the manual technique. Methods: Twenty-eight patients who underwent mandibular setback surgery (BSSRO) were randomly selected between 2015 and 2021. Serial lateral cephalograms were analyzed at four different time sets postoperatively, and a total of 112 cephalometric radiographs were obtained. Horizontal measurement (BX), vertical measurement (BY), and 3 angular measurements (SNB, ANB, and Gonial angle) were analyzed by manual tracing and Dolphin software by 2 examiners. The intraclass correlation coefficient determined the intra-rater reliability. Parameter differences between timelines were observed for skeletal stability, and mean values between methods were compared using the Student's t-test. Results: Both examiners were generally consistent in the repeated measurements (ICCs of the manual method ranged from 0.926 to 0.994, and the digital method ranged from 0.719 to 0.956). All variables represented skeletal stability at T0-T1, T0-T2, and T0-T3 showed no statistically significant differences between methods except ANB (T0-T1; p value = 0.009). Conclusions: Computerized cephalometric analysis software is relatively reproducible for assessing skeletal changes after orthognathic surgery and can be used routinely in follow-up.

Dentofacial characteristics of children and adolescents with foetal alcohol spectrum disorders: a comparison with matched controls.

BACKGROUND: Foetal alcohol spectrum disorders (FASD) include somatic and neurological developmental disturbances after prenatal alcohol exposure, including facial anomalies. However, the knowledge of the orthodontic skeletal and dental cephalometric relations in this group is limited. The aim of the study was to assess the dentofacial characteristics of children and adolescents with FASD and to compare them with a matched control group. METHODS: The study group comprised all available children and adolescents diagnosed with FASD (> 7 years of age) in whom good quality cephalograms were available. The control group comprised non-syndromic, orthodontically untreated children with normal occlusion and skeletal relations matched with age and gender. Cephalometric analysis included eighteen linear and angular measurements. The general linear model for repeated measures regarding age, gender and the type of FASD was applied. RESULTS: The group with FASD included 35 individuals (21 girls and 14 boys) aged 7-18 years including 21 with foetal alcohol syndrome. The mean age in the study and the control group was 12.8 years (SD, range 3.2, 7.1-18.1) and 13.0 (SD, range 2.9, 9.1-18.1), respectively. Statistically significant differences between the groups were found in 15 out of 18 of the cephalometric measurements (83%). In children with FASD the mandible was more retrusive, the incisors were more proclined and the mandibular incisors and the lips were more protruded when compared with controls. There was no significant evidence of an influence of age, gender or FASD type. CONCLUSIONS: Dentofacial characteristics of children and adolescents with FASD significantly differ from controls. Early orthodontic diagnosis and prophylaxis should play a part of the interdisciplinary care of patients in this group.

Skull Biomechanics and Simplified Cephalometric Lines for the Estimation of Muscular Lines of Action.

Our study introduces a novel cephalometric analysis aimed at facilitating biomechanical simulations by elucidating the intricate relationship between craniofacial morphology and the size and inclination of the masseter muscle (MM) while incorporating muscle values. Our study analyzes the line of action of the MM drawn between the Gonion (Go) and Orbital (Or) points concerning dental and skeletal references (occlusal and Frankfort planes). A total of 510 pre-treatment lateral cephalometric tracings (217 males, 293 females, aged 6-50 years) and lateral Bolton standard tracings were examined. The key parameters investigated include (a) skeletal-cutaneous class (linear distance between projections of points A' and B' on the occlusal plane), (b) the angle between the perpendicular line to the occlusal plane and the Go-Or line at the molar occlusal point, and (c) the angle between the Go-Or line and the Frankfort plane. The assessment of anterior-posterior jaw discrepancy, measured as the skeletal-cutaneous class, ranged from -14.5 to 15.5 mm. Abnormal values were identified in two adolescents, showing no gender- or age-related patterns. The angle between the MM's line of action (Go-Or) and the normal to the occlusal plane averaged 39.3°, while the angle between Go-Or and Po-Or (Frankfort plane) averaged 41.99°. Age had an impact on these angles, with an average 3° decrease in adults and a 4° increase between ages 6 and 50. A weak relationship was observed between sagittal jaw discrepancy and the angle between Go-Or and the Frankfort plane, with about 20% of the variance explained by the anteroposterior maxillary-mandibular relationship. In conclusion, the study presents a cephalometric analysis of the relationship between craniofacial morphology and masseter muscle parameters. It finds that age influences the angles between key reference points, while the skeletal-cutaneous class does not exhibit age- or gender-specific trends. These findings can contribute to a better understanding of craniofacial biomechanics and aid in clinical orthodontic assessments and treatment planning.

[Lower jawbone structures symmetry evaluation using cone beam computed tomography]. / Otsenka simmetrichnosti kostnykh struktur nizhnei chelyusti po dannym konusno-luchevoi komp'yuternoi tomografii.

OBJECTIVE: To assess the prevalence and quantitative characteristics of skeletal asymmetries of the body and lower jaw branches in three-dimensional space. MATERIALS AND METHODS: Using depersonalized data archive, of 400 CBCT scans, forty studies were picked randomly. Patients aged 25 to 35 years participated in research, no history of maxillofacial reconstructive surgery; gender, race, and facial anomaly were ignored. Three experienced doctors, independently, identified 15 reference points on radiographic images of AUTOPLAN software. Expert consensus found in the orientation determination. Six planes and eight points were constructed, using Math algorithms and the Python OOP; body and branches of the lower jaw length measurements performed and processed automatically. RESULTS: The study identified symmetry, using a method for determining parameters of the lower jaw in 3D space (Priority Reference No.2023100466 at 10.01.2023), modern dentistry research method. Both sides of data obtained shown asymmetry of the body and branches of the lower jaw occurred in 45% (18 people) and 67.5% (27 people) of cases. Forty percent of cases were noted with Mild asymmetry of the lower jaw body (16 people), moderate asymmetry noted in 5% (2 people). These parameters were 57.5% for branches, (23 people) and 10% (4 people). Combined form of anomaly observed in 37.5% (15 people) of cases, among which 12 people (75%) showed adaptive compensation of anomalies, which requires further investigation on a larger number of patients. CONCLUSION: Lower body jaw asymmetry is prevalent (80%). Patients with moderate and serious anomaly form appeared in 15% of cases.

Automatic craniomaxillofacial landmarks detection in CT images of individuals with dentomaxillofacial deformities by a two-stage deep learning model.

BACKGROUND: Accurate cephalometric analysis plays a vital role in the diagnosis and subsequent surgical planning in orthognathic and orthodontics treatment. However, manual digitization of anatomical landmarks in computed tomography (CT) is subject to limitations such as low accuracy, poor repeatability and excessive time consumption. Furthermore, the detection of landmarks has more difficulties on individuals with dentomaxillofacial deformities than normal individuals. Therefore, this study aims to develop a deep learning model to automatically detect landmarks in CT images of patients with dentomaxillofacial deformities. METHODS: Craniomaxillofacial (CMF) CT data of 80 patients with dentomaxillofacial deformities were collected for model development. 77 anatomical landmarks digitized by experienced CMF surgeons in each CT image were set as the ground truth. 3D UX-Net, the cutting-edge medical image segmentation network, was adopted as the backbone of model architecture. Moreover, a new region division pattern for CMF structures was designed as a training strategy to optimize the utilization of computational resources and image resolution. To evaluate the performance of this model, several experiments were conducted to make comparison between the model and manual digitization approach. RESULTS: The training set and the validation set included 58 and 22 samples respectively. The developed model can accurately detect 77 landmarks on bone, soft tissue and teeth with a mean error of 1.81 ± 0.89 mm. Removal of region division before training significantly increased the error of prediction (2.34 ± 1.01 mm). In terms of manual digitization, the inter-observer and intra-observer variations were 1.27 ± 0.70 mm and 1.01 ± 0.74 mm respectively. In all divided regions except Teeth Region (TR), our model demonstrated equivalent performance to experienced CMF surgeons in landmarks detection (p > 0.05). CONCLUSIONS: The developed model demonstrated excellent performance in detecting craniomaxillofacial landmarks when considering manual digitization work of expertise as benchmark. It is also verified that the region division pattern designed in this study remarkably improved the detection accuracy.

Stability of anterior open bite cases treated with upper and lower extrusion arches in adults: a follow-up study.

OBJECTIVES: To investigate the post-treatment stability of anterior open bite (AOB) cases treated with upper and lower extrusion arches in adults. MATERIALS AND METHODS: 23 patients with AOB were treated with extrusion arches and evaluated for dentoskeletal changes using lateral cephalometric radiographs before treatment (T0), after treatment (T1), and 12 months post-treatment (T2). Patients received bondable tongue spurs and vacuum-formed retainers as post-treatment retention/stability measures for 12 months. Post-treatment stability was further assessed qualitatively by the Photographic Openbite Severity Index. RESULTS: All the cases showed positive overlap between the anterior teeth at the end of orthodontic treatment, and 20 of 23 treated patients attended the recall visit after 12 months. A total of 85% of the cases maintained positive overlap at least 1 year post-treatment, 10% showed edge-to-edge bite, and only 5% showed no overbite. CONCLUSIONS: Treatment of AOB in adults with extrusion arches proved to be stable 1 year post-treatment. Changes in the overbite after 1 year of follow-up were not significant.

An MRI-only Three-dimensional Cephalometry Protocol based on the Integrated and Modular Architecture of the Human Head.

BACKGROUND: Currently, three-dimensional cephalometry measurements are mainly based on cone beam computed tomography (CBCT), which has limitations of ionizing radiation, lack of soft tissue information, and lack of standardization of median sagittal plane establishment. OBJECTIVES: This study investigated magnetic resonance imaging (MRI)-only based 3D cephalometry measurement based on the integrated and modular characteristics of the human head. METHODS: Double U-Net CycleGAN was used for CT image synthesis from MRI. This method enabled the synthesis of a CT-like image from MRI and measurements were made using 3D slicer registration and fusion. RESULTS: A protocol for generating and optimizing MRI-based synthetic CT was described and found to meet the precision requirements of 3D head measurement using MRI midline positioning methods reported in neuroscience to establish the median sagittal plane. An MRI-only reference frame and coordinate system were established enabling an MRI-only cephalometric analysis protocol that combined the dual advantages of soft and hard tissue display. The protocol was devised using data from a single volunteer and validation data from a larger sample remains to be collected. CONCLUSION: The reported method provided a new protocol for MRI-only cephalometric analysis of craniofacial growth and development, malformation occurrence, treatment planning, and outcomes.

Ceph-Net: automatic detection of cephalometric landmarks on scanned lateral cephalograms from children and adolescents using an attention-based stacked regression network.

BACKGROUND: The success of cephalometric analysis depends on the accurate detection of cephalometric landmarks on scanned lateral cephalograms. However, manual cephalometric analysis is time-consuming and can cause inter- and intra-observer variability. The purpose of this study was to automatically detect cephalometric landmarks on scanned lateral cephalograms with low contrast and resolution using an attention-based stacked regression network (Ceph-Net). METHODS: The main body of Ceph-Net compromised stacked fully convolutional networks (FCN) which progressively refined the detection of cephalometric landmarks on each FCN. By embedding dual attention and multi-path convolution modules in Ceph-Net, the network learned local and global context and semantic relationships between cephalometric landmarks. Additionally, the intermediate deep supervision in each FCN further boosted the training stability and the detection performance of cephalometric landmarks. RESULTS: Ceph-Net showed a superior detection performance in mean radial error and successful detection rate, including accuracy improvements in cephalometric landmark detection located in low-contrast soft tissues compared with other detection networks. Moreover, Ceph-Net presented superior detection performance on the test dataset split by age from 8 to 16 years old. CONCLUSIONS: Ceph-Net demonstrated an automatic and superior detection of cephalometric landmarks by successfully learning local and global context and semantic relationships between cephalometric landmarks in scanned lateral cephalograms with low contrast and resolutions.

Correlation Analysis of Nasal Septum Deviation and Results of AI-Driven Automated 3D Cephalometric Analysis.

The nasal septum is believed to play a crucial role in the development of the craniofacial skeleton. Nasal septum deviation (NSD) is a common condition, affecting 18-65% of individuals. This study aimed to assess the prevalence of NSD and its potential association with abnormalities detected through cephalometric analysis using artificial intelligence (AI) algorithms. The study included CT scans of 120 consecutive, post-traumatic patients aged 18-30. Cephalometric analysis was performed using an AI web-based software, CephX. The automatic analysis comprised all the available cephalometric analyses. NSD was assessed using two methods: maximum deviation from an ideal non-deviated septum and septal deviation angle (SDA). The concordance of repeated manual measurements and automatic analyses was assessed. Of the 120 cases, 90 met the inclusion criteria. The AI-based cephalometric analysis provided comprehensive reports with over 100 measurements. Only the hinge axis angle (HAA) and SDA showed significant (p = 0.039) negative correlations. The rest of the cephalometric analyses showed no correlation with the NSD indicators. The analysis of the agreement between repeated manual measurements and automatic analyses showed good-to-excellent concordance, except in the case of two angular measurements: LI-N-B and Pr-N-A. The CephX AI platform showed high repeatability in automatic cephalometric analyses, demonstrating the reliability of the AI model for most cephalometric analyses.

Cephalometric analysis in adults with tympanic membrane atelectasis.

OBJECTIVE: This study aims to examine craniofacial morphology (CFM) with cephalometric radiographs in adults with tympanic membrane atelectasis (TMA). METHODS: A total of 50 patients (study group) were included and standardized lateral and anteroposterior cephalograms were performed. The control group consisted of 50 patients. Anteroposterior cephalograms were compared with the normative data. RESULTS: A significant increase in vertical and decrease in horizontal linear measurements were observed in lateral and anteroposterior cephalograms respectively. Except for the facial axis, the angular measurements were not statistically significant. CONCLUSION: Cephalometric data indicate a narrow and elongated but nearly proportional CFM for patients with TMA.

Application of Artificial Intelligence (AI) in a Cephalometric Analysis: A Narrative Review.

In recent years, the application of artificial intelligence (AI) has become more and more widespread in medicine and dentistry. It may contribute to improved quality of health care as diagnostic methods are getting more accurate and diagnostic errors are rarer in daily medical practice. The aim of this paper was to present data from the literature on the effectiveness of AI in orthodontic diagnostics based on the analysis of lateral cephalometric radiographs. A review of the literature from 2009 to 2023 has been performed using PubMed, Medline, Scopus and Dentistry & Oral Sciences Source databases. The accuracy of determining cephalometric landmarks using widely available commercial AI-based software and advanced AI algorithms was presented and discussed. Most AI algorithms used for the automated positioning of landmarks on cephalometric radiographs had relatively high accuracy. At the same time, the effectiveness of using AI in cephalometry varies depending on the algorithm or the application type, which has to be accounted for during the interpretation of the results. In conclusion, artificial intelligence is a promising tool that facilitates the identification of cephalometric landmarks in everyday clinical practice, may support orthodontic treatment planning for less experienced clinicians and shorten radiological examination in orthodontics. In the future, AI algorithms used for the automated localisation of cephalometric landmarks may be more accurate than manual analysis.

Evaluation of optimal anterior-posterior position of upper incisors through ANS point: a retrospective study on a Chinese sample.

OBJECTIVES: This study was designed to determine the optimal anterior-posterior (AP) position of upper incisors through Anterior Nasal Spine (ANS) point. MATERIALS AND METHODS: Lateral cephalometric radiographic images of 690 patients were collected and divided into a derivation group and a validation group, and the former were subdivided into a proper AP position (PAP) group and an improper AP position (iPAP) group. The distance from facia-axis (FA) point of upper incisors to the line perpendicular to Frankfort horizontal (FH) plane through ANS (FA-ANS) was measured, and the relationship between FA-ANS and several cephalometric indices were studied through Pearson correlation analysis. Receiver operating characteristic (ROC) curves for different clinical indices were analyzed to evaluate the diagnostic efficiency of optimal AP position of upper incisors. RESULTS: The average value of FA-ANS in PAP group was 0.57±1.99, which was significantly different from FA-ANS in iPAP group. Cephalometric indices such as U1-NA, U1-SN, AB-NPo, UL-TVL, Wits, and ANB were found to be correlated with FA-ANS. The receiver operating characteristic (ROC) curves represented a greater diagnostic efficiency of FA-ANS compared with other clinical indices. CONCLUSIONS: ANS point, as a stable skeletal landmark, could be used to access an optimal AP position of upper incisors, providing aids to clinical diagnosis and treatment goal determination for clinical practice. CLINICAL RELEVANCE: A new index FA-ANS, together with other traditional indices, could help determine the optimal position of upper incisors and provide a personalized therapeutic plan.