Inter-observer and intra-observer agreement of cervical vertebral maturation staging: A systematic review and meta-analysis.

INTRODUCTION: The accurate assessment of active growth is pivotal for the correction of skeletal malocclusion in growing patients. Cervical vertebral maturation (CVM) staging is easy and devoid of unnecessary radiation, but its inter- and intra-observer agreement is still debatable. OBJECTIVE: This study aims to assess inter-observer (reliability) and intra-observer agreement (reproducibility) for CVM staging. METHODS: A comprehensive literature search across five databases up to October 2023 was conducted. Inclusion criteria comprised observational studies that specifically reported intra-rater, inter-rater, or both agreements as their primary objectives for cervical vertebral maturation (CVM) staging. Studies with a sample size exceeding 15 participants were considered for inclusion. After duplicate study selection, data extraction, and risk-of-bias assessment, random-effects meta-analyses of kappa (k)/correlation coefficient (r) and their 95% confidence intervals (CIs) were performed, followed by meta-regressions, sensitivity analyses, and subgroup analyses. RESULTS: Seventeen observational studies (comprising 1437 lateral cephalograms and 110 assessors) were included. The reliability (8 studies; k=0.62 [95% CI: 0.44, 0.78]) and reproducibility (9 studies; k=0.708 [95% CI: 0.59, 0.82]) were substantial. The inter-observer correlation was almost perfect (in 9 studies; r=0.86 [95% CI: 0.82, 0.89]) while intra-observer correlation was substantial (in 2 studies; r=0.75 [95% CI: 0.62, 0.84]). Tracing of lateral cephalograms significantly increased inter-observer reliability (ß=0.29 [0.57, 0.0031]) but cropping and time of assessment (initial vs. follow-up) had no significant impact. The reliability (P<0.001) and reproducibility (P=0.049) of high-quality studies were superior to those observed in low-quality studies. CONCLUSION: Assessment of the CVM staging is accurate and reproducible to a satisfactory level. The accuracy and reproducibility of CVM are higher in studies utilizing traced cephalograms and those with a low-risk of bias. REGISTRATION: PROSPERO registration (CRD42023468521). Data is openly available at https://doi.org/10.5281/zenodo.10599129.

CURRENT NEURAL NETWORKS DEMONSTRATE POTENTIAL IN AUTOMATED CERVICAL VERTEBRAL MATURATION STAGE CLASSIFICATION BASED ON LATERAL CEPHALOGRAMS.

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Neural networks for classification of cervical vertebrae maturation: a systematic review. Mathew R, Palatinus S, Padala S, Alshehri A, Awadh W, Bhandi S, Thomas J, Patil S. Angle Orthod. 2022 Nov 1;92(6):796-804. SOURCE OF FUNDING: No financial support was reported. TYPE OF STUDY/DESIGN: Systematic review.

Dental and skeletal maturation of Chinese male children with unilateral cleft lip and palate.

The aim of this study was to determine whether the relationship between dental age (DA), cervical stage (CS) and chronological age (CA) in Chinese male children with unilateral cleft lip and palate (UCLP) is similar to that of children without clefts. Panoramic and cephalometric radiographs of 105 male UCLP patients, aged from 8 to 16 years, were collected and compared to 210 age-matched healthy control males. The Demirjian and cervical vertebral maturation (CVM) methods were used to visually examine the radiographs and Spearman's correlation analysis was used to identify differences between the two groups with regards to CS, DA and CA. There was a significant positive correlation between DA and CA in both groups and the mean CA-DA difference was significantly higher in children with UCLP when compared to controls (0.319 vs. 0.003, p < 0.05). A significant delay in tooth development was detected in UCLP children from 10 to 12 years-of-age. Both the UCLP and control groups showed high correlations between CS and DA. Calcification stage D appeared only before CS3; however, from CS5 to 6, all teeth have almost completed their maturation phase. Chinese male UCLP patients are likely to experience delayed tooth development compared to healthy controls, especially during the fast-growing period. Evaluating the stages of tooth mineralization could represent a rapid method to assess growth potential.

Correlation between dental and skeletal maturity in Korean children based on dental maturity percentile: a retrospective study.

BACKGROUND: The correlation between dental maturity and skeletal maturity has been proposed, but its clinical application remains challenging. Moreover, the varying correlations observed in different studies indicate the necessity for research tailored to specific populations. AIM: To compare skeletal maturity in Korean children with advanced and delayed dental maturity using dental maturity percentile. DESIGN: Dental panoramic radiographs and cephalometric radiographs were obtained from 5133 and 395 healthy Korean children aged between 4 and 16 years old. Dental maturity was assessed with Demirjian's method, while skeletal maturity was assessed with the cervical vertebral maturation method. Standard percentile curves were developed through quantile regression. Advanced (93 boys and 110 girls) and delayed (92 boys and 100 girls) dental maturity groups were defined by the 50th percentile. RESULTS: The advanced group showed earlier skeletal maturity in multiple cervical stages (CS) in both boys (CS 1, 2, 3, 4, and 6) and girls (CS 1, 3, 4, 5, and 6). Significant differences, as determined by Mann-Whitney U tests, were observed in CS 1 for boys (p = 0.004) and in CS 4 for girls (p = 0.037). High Spearman correlation coefficients between dental maturity and cervical vertebral maturity exceeded 0.826 (p = 0.000) in all groups. CONCLUSION: A correlation between dental and skeletal maturity, as well as advanced skeletal maturity in the advanced dental maturity group, was observed. Using percentile curves to determine dental maturity may aid in assessing skeletal maturity, with potential applications in orthodontic diagnosis and treatment planning.

Evaluation of an objective staging system for assessment of cervical vertebral maturation.

BACKGROUND: The aim of this study was to evaluate an objective method for Cervical Vertebral Maturation (CVM) staging. METHODS: An initial sample of 647 Lateral Cephalometric Radiographs (LCR) were staged according to the CVM (Baccetti et al.) by 4 examiners. The final sample (n = 394) included LCR on which the staging of the 4 investigators matched. The objective staging was performed by a single operator. The sample was divided according to the maturational stages into pre-pubertal, pubertal and post-pubertal groups. Measurements were performed on the cervical vertebrae (C2, C3 and C4). The angle between posterior and superior borders for C3 and C4 was the Superior Wall Inclination Angle (SWIA). Concavity Depth (CD) for C2, C3 and C4, and Body Shape (BS) (ratio of width to height of C3 and C4). Measurements of the 3 groups were compared. RESULTS: Reliability of subjective staging was high (intra-observer reliability, 0.948; inter-observer reliability, 0.967). Good agreement was observed for the outcomes measured. Intra-observer reliability was good (0.918, 0.885 and 0.722 for CD, BS and SWIA, respectively). The same was for the inter-observer reliability results (0.902, 0.889 and 0.728 for CD, BS and SWIA, respectively). Significant differences were observed for mean values of SWIA and BS and median values of CD within maturational stage. Similar findings were observed when the outcomes were compared at different phases (P < 0.001). CONCLUSIONS: A standardized, objective staging system using linear, angular measurements and ratios was applied for the determination of cervical vertebral maturation.

Assessment of correlation between hand-wrist maturation and cervical vertebral maturation: a fractal analysis study.

BACKGROUND: To investigate whether fractal dimension (FD) measurements from hand-wrist radiographs and lateral cephalometric radiographs are correlated with each other and with skeletal maturation stages. METHODS: In this retrospective study conducted on hand-wrist and lateral cephalometric radiographs obtained from patients between 2017 and 2023, hand-wrist maturation stages (HWMS) and cervical vertebral maturation stages (CVMS) of 144 subjects (6 to 17 years of age) were assessed radiographically. The participants were divided into nine groups (n = 16 each) based on HWMS. Fractal analysis was performed on the radiographs of the radius, the middle finger phalanges (proximal, medial and distal), and the cervical vertebral bodies (C2, C3, C4). Mean and standard deviation values, Spearman's and Pearson correlation analyses, one-way ANOVA, Kruskal-Wallis H tests and Mann-Whitney-U test were used to evaluate the data. RESULTS: Positive correlations were found between the FD values of the radius and HWMS or CVMS (r = .559, P = .001, r = .528 P = .001 respectively). The FD values of the radius were positively correlated with those of all cervical vertebrae (C2, C3, C4), proximal and medial phalanges as well as age. FD values measured from the proximal phalanx, medial phalanx and radius showed significant differences among both HWMS and CVMS (P < .05). HWMS was strongly correlated with CVMS (r = .929, P = .001). Age was strongly correlated with HWMS (r = .795, P = .001) and CVMS (r = .756, P = .001). There was a significant difference in terms of age distribution among HWMS and CVMS (P < .05). CONCLUSIONS: FD measurements on hand-wrist radiographs can provide useful information for the assessment of skeletal maturation stage. Especially, FD measurements from the radius are important and more reliable to predict skeletal maturation stage.

Evaluation of Cervical Vertebral Maturation Angular Measurements as a Tool to Assess Skeletal Maturity: A Radiological Study.

Aim: The purpose of the present research was to evaluate the comparison between the Skeletal Maturation phases of Fishman's hand-wrist skeletal maturation index (SMI) and cervical vertebral maturation angle (CVMA). Methodology: Assessment and comparison of Skeletal Maturation utilizing the hand-wrist bone radiograph by Fishman's system of skeletal maturation indicator (SMI), cervical vertebra maturation stages (CVMS), and lateral cephalogram by cervical vertebral maturation angle (CVMA) conducted on 150 subjects (75 male and 75 female). Chi-square was used to reveal the significance of study parameters on a categorical scale between SMI, CVMS, CVMA, and multiple regression analysis conducted to envisage the connotation of the dependent variable. Results: Multiple regression analysis models exhibited 61.9% of the variance for CVMA with autonomous variables. In multiple regression analysis, there is no substantial association (P > 0.05) between age and CVMA. The correlation of SMI and CVMA values utilizing Pearson's correlation coefficient for males and females presented positive association with R value of 0.801 for males and 0.834 for females and a P value of < 0.001. Conclusion: As compared to other maturity indicators like hand and wrist, cervical vertebral angular measurements were reliable enough for assessing the degree of skeletal maturation.

Cervical Vertebral Maturation Assessment using various Machine Learning techniques on Lateral cephalogram: A systematic literature review.

Importance: For the assessment of optimum treatment timing in dentofacial orthopedics, understanding the growth process is of paramount importance. The evaluation of skeletal maturity based on study of the morphology of the cervical vertebrae has been devised to minimize radiation exposure of a patient due to hand wrist radiography. Cervical vertebral maturation assessment (CVMA) predictions have been examined in the state-of-the-art machine learning techniques in the recent past which require more attention and validation by clinicians and practitioners. Objectives: This paper aimed to answer the question "How are machine learning techniques being employed in studies concerning cervical vertebral maturation assessment using lateral cephalograms?" Method: A systematic search through the available literature was performed for this work based upon the Population, Intervention, Comparison and Outcome (PICO) framework. Data sources study selection data extraction and synthesis: The searches were performed in Ovid Medline, Embase, PubMed and Cochrane Central Register of Controlled Trials (CENTRAL) and Cochrane Database of Systematic Reviews (CDSR). A search of the grey literature was also performed in Google Scholar and OpenGrey. We also did a hand-searching in the Angle Orthodontist, Journal of Orthodontics and Craniofacial Research, Progress in Orthodontics, and the American Journal of Orthodontics and Dentofacial Orthopedics. References from the included articles were also searched. Main outcomes and measures results: A total of 25 papers which were assessed for full text, and 13 papers were included for the systematic review. The machine learning methods used were scrutinized according to their performance and comparison to human observers/experts. The accuracy of the models ranged between 60 and 90% or above, and satisfactory agreement and correlation with the human observers. Conclusions and relevance: Machine learning models can be used for detection and classification of the cervical vertebrae maturation. In this systematic review (SR), the studies were summarized in terms of ML techniques applied, sample data, age range of sample and conventional method for CVMA, which showed that further studies with a uniform distribution of samples equally in stages of maturation and according to the gender is required for better training of the models in order to generalize the outputs for prolific use to target population.

The psc-CVM assessment system: A three-stage type system for CVM assessment based on deep learning.

BACKGROUND: Many scholars have proven cervical vertebral maturation (CVM) method can predict the growth and development and assist in choosing the best time for treatment. However, assessing CVM is a complex process. The experience and seniority of the clinicians have an enormous impact on judgment. This study aims to establish a fully automated, high-accuracy CVM assessment system called the psc-CVM assessment system, based on deep learning, to provide valuable reference information for the growth period determination. METHODS: This study used 10,200 lateral cephalograms as the data set (7111 in train set, 1544 in validation set and 1545 in test set) to train the system. The psc-CVM assessment system is designed as three parts with different roles, each operating in a specific order. 1) Position Network for locating the position of cervical vertebrae; 2) Shape Recognition Network for recognizing and extracting the shapes of cervical vertebrae; and 3) CVM Assessment Network for assessing CVM according to the shapes of cervical vertebrae. Statistical analysis was conducted to detect the performance of the system and the agreement of CVM assessment between the system and the expert panel. Heat maps were analyzed to understand better what the system had learned. The area of the third (C3), fourth (C4) cervical vertebrae and the lower edge of second (C2) cervical vertebrae were activated when the system was assessing the images. RESULTS: The system has achieved good performance for CVM assessment with an average AUC (the area under the curve) of 0.94 and total accuracy of 70.42%, as evaluated on the test set. The Cohen's Kappa between the system and the expert panel is 0.645. The weighted Kappa between the system and the expert panel is 0.844. The overall ICC between the psc-CVM assessment system and the expert panel was 0.946. The F1 score rank for the psc-CVM assessment system was: CVS (cervical vertebral maturation stage) 6 > CVS1 > CVS4 > CVS5 > CVS3 > CVS2. CONCLUSIONS: The results showed that the psc-CVM assessment system achieved high accuracy in CVM assessment. The system in this study was significantly consistent with expert panels in CVM assessment, indicating that the system can be used as an efficient, accurate, and stable diagnostic aid to provide a clinical aid for determining growth and developmental stages by CVM.

Individual dental and skeletal age assessment according to Demirjian and Baccetti: Updated norm values for Central-European patients.

PURPOSE: Chronological age often differs from dental and skeletal age. With orthopantomograms and lateral cephalograms, dental and skeletal development can be determined according to the methods published by Demirjian et al. and Baccetti et al. However, gender and skeletal class as possible confounders were frequently not considered and available norm values are not up-to-date. This retrospective cross-sectional study thus aimed to evaluate effects of skeletal class and gender on dental and skeletal age of growing patients and to generate updated norm values for contemporary Central-European patients. METHODS: A total of 551 patients were included in the dental and 733 in the skeletal age assessment, respectively. Dental analysis was based on tooth mineralisation stages in orthopantomograms (Demirjian) and skeletal age was defined by cervical vertebrae maturation stages (CVMS) in lateral cephalograms (Baccetti). Skeletal class was determined by the individualised ANB angle of Panagiotidis/Witt. With nonlinear regression analysis a formula for determining dental age was established. Effects of gender and skeletal class were evaluated and updated norm values generated. RESULTS: Inter- and intrarater reliability tests revealed at least substantial measurement concordance for tooth mineralisation and CVMS. Demirjian stages and CVMS significantly depended on gender with girls developing earlier. Skeletal class significantly affected skeletal age only, but without clinical relevance. Updated norm values for dental age differed significantly from the original values of Demirjian and the values for skeletal age differed from those published by Baccetti. CONCLUSION: Optimised norms, separated by gender, increase precision in determining individual dental and skeletal age during orthodontic treatment planning. Further studies analysing the effect of skeletal class on dental and skeletal development are needed.

Convolutional neural network-based automatic cervical vertebral maturation classification method.

OBJECTIVES: This study aimed to develop a fully automated artificial intelligence-aided cervical vertebral maturation (CVM) classification method based on convolutional neural networks (CNNs) to provide an auxiliary diagnosis for orthodontists. METHODS: This study consisted of cephalometric images from patients aged between 5 and 18 years. After grouping them into six cervical stages (CSs) by orthodontists, a data set was constructed for analyzing CVM using CNNs. The data set was divided into training, validation, and test sets in the ratio of 70, 15, and 15%. Four CNN models namely, VGG16, GoogLeNet, DenseNet161, and ResNet152 were selected as the candidate models. After training and validation, the models were evaluated to determine which of them is most suitable for CVM analysis. Heat maps were analyzed for a deeper understanding of what the CNNs had learned. RESULTS: The final classification accuracy ranking was ResNet152>DenseNet161>GoogLeNet>VGG16, as evaluated on the test set. ResNet152 proved to be the best model among the four models for CVM classification with a weighted κ of 0.826, an average AUC of 0.933 and total accuracy of 67.06%. The F1 score rank for each subgroup was: CS6>CS1>CS4>CS5>CS3>CS2. The area of the third (C3) and fourth (C4) cervical vertebrae were activated when CNNs were assessing the images. CONCLUSION: CNN models proved to be a convenient, fast and reliable method for CVM analysis. CNN models have the potential to provide automatic auxiliary diagnostic tools in the future.

Effects of mouth breathing on maxillofacial and airway development in children and adolescents with different cervical vertebral maturation stages: a cross-sectional study.

BACKGROUND: To examine the influence of mouth breathing on maxillofacial and airway development in children and adolescents with different cervical vertebral maturation stages. METHODS: Lateral cephalometric radiograph of a total of 120 children and adolescents, 64 girls and 56 boys (7-15 years old), diagnosed with mouth breathing were examined. Maxillofacial hard tissue, soft tissue and airway measurements were obtained using both manual and digital techniques. Independent samples t-test was performed to compare the difference between the measured indexes and the standard values. RESULTS: As for maxillofacial hard tissue, SNB (CS1-CS5), GoGn (CS1-CS5), ArGoNa (CS1-CS5), ArGo (CS1-CS2) and SNA (CS1-CS2) in mouth breathing children and adolescents were below the standard values (P < 0.05). NGoMe (CS1-CS5), SN-MP (CS1-CS4), SN-PP (CS1-CS4), PP-MP (CS1-CS3) and SN-GoGn (CS1-CS2) in mouth breathing children and adolescents were above the standard values (P < 0.05). As for maxillofacial soft tissue measurements, H angle (CS1-CS5), lower lip length (CS1-CS5), upper lip protrusion (CS1-CS5), upper lip length (CS1-CS4), lower lip protrusion (CS1-CS3), surface Angle (CS2-CS3) and nasolabial angle (CS2) in mouth breathing children and adolescents were above the standard values with statistically significance (P < 0.05). As for airway measurements, PAS (CS1, CS2, CS5) in mouth breathing children and adolescents was above the standard value with statistical significance (P < 0.05). CONCLUSIONS: Mouth breathing had a real effect on maxillofacial and airway development, which differed among mouth breathing children and adolescents with different cervical vertebral maturation.

Concordances and correlations between chronological, dental and bone ages: A retrospective study in French individuals.

Bone and dental maturations are subject to physiological, environmental and pathological variables and are unique to each individual. Numerous methods for age estimation appeared to answer a new demand explained by the contemporary migratory movements and the increase of the crime rates. The objective of this study was to observe the relationships between skeletal and dental maturation stages to estimate which methods can be applied to a French population. This retrospective study was based on panoramic and cephalometric radiographs belonging to 192 (101 females and 91 males) orthodontic patients aged between 9 and 19 years, inhabitants of the Provence-Alpes-Côte d'Azur region. The Demirjian method based on dental calcification was used to estimate dental age. For the assessment of skeletal maturity and bone age, the Cervical Vertebral Maturation (CVM) method (Baccetti et al.) and its computerized version (Decocq et al.) were used. Spearman's correlation tests were performed to estimate the correlation between dental calcification stages and those of cervical vertebral maturation among the study population. The methods of Lin's concordance correlation coefficient and Bland and Altman were used to analyze the concordance between bone, dental and chronological ages. A moderate but statistically significant correlation was obtained between dental and skeletal developmental stages (R = 0.383-0.618). The tooth showing the highest correlation with vertebral stages was the second molar for both sexes. Earlier vertebral maturation stage affiliation was observed in girls. The results confirmed that bone and dental maturations can be assessed to estimate relevant biological ages for children in orthodontic or forensic contexts.

Can IGF-1 serve as a reliable skeletal maturity indicator? A meta-analysis.

BACKGROUND: Many clinical studies have evaluated the role of biochemical mediators like insulin-like growth factor 1 (IGF-1) in assessment of skeletal maturity. But still the reliability of IGF­1 as an indicator of skeletal maturity remains controversial. OBJECTIVE: To assess the correlation between IGF­1 and different radiographic skeletal maturity indicators. SEARCH METHODS: Seven electronic databases (PubMed, Scopus, EMBASE, Medline, Cochrane CENTRAL, Web of Science and SciELO) were searched until January 2020 without any restriction based on language or date of publication. SELECTION CRITERIA: The study design included cross-sectional and longitudinal studies comparing IGF­1 and other skeletal maturity indicators (SMIs). DATA COLLECTION AND ANALYSIS: Data extraction was done by two reviewers independently; 15 studies were eligible to be included in the quantitative synthesis. RESULTS: There was significant positive correlation between IGF­1 and different SMIs until puberty which was 0.95 (confidence interval [CI] = 0.89, 1.02) for males and 0.87 (CI = 0.77, 0.97) for females. A negative correlation between IGF­1 and different SMIs was found after puberty which was -0.86 (CI = -0.97, -0.75) for males and -0.89 (CI = -0.98, -0.81) for females. The type of SMI compared and type of IGF­1 sample used accounted for the high heterogeneity found across the studies. Chronological age and number of months passed after puberty showed moderate negative correlation with mean IGF­1 levels which were -0.57 (CI = -0.67, -0.47) and -0.54 (CI = -0.66, -0.42). Annual increments in mandibular length showed significant positive correlation of 0.69 (CI = 0.48, 0.90). CONCLUSIONS: IGF­1 would serve as a promising alternative to conventional radiographic skeletal maturity indicators and in predicting the amount of residual mandibular growth.

Intelligent quantitative assessment of skeletal maturation based on multi-stage model: a retrospective cone-beam CT study of cervical vertebrae.

OBJECTIVE: To develop new logistic regression estimative models of the cervical vertebral maturation index (CVMI) based on cone-beam CT (CBCT)-derived parameters for intelligent evaluating skeletal maturation. METHODS: From 231 CBCT volumes (age range 7-17, mean age 11.09 years), 154 were randomly selected to produce 2D sagittal projections of the second to fourth cervical vertebrae (C2-C4). From 19 quantitative parameters, significant predictors were deduced to formulate logistic models. Using the CVMI and significant predictors of 77 other subjects, performance of the models was externally examined by direct comparison and the area under the receiver operating characteristic curve (AUC). Models were modified if required, to improve their accuracy. RESULTS: Chronological age, C3 height ([Formula: see text], and ratio of posterior height to lower width of C4 [Formula: see text] were entered as significant predictors. Accuracy of the models was acceptable (total AUC = 0.91) except for 4th and 5th stage (AUC of 0.82 and 0.83, respectively), which were mis-predicted inversely. Adjusted models were generated by bivariate logistic regression analysis and adding significant parameters ([Formula: see text] and [Formula: see text], with odds ratios of 3.308 and 3.38, respectively) from 58 subjects in 4th and 5th stages of CVMI in the model establishment group. The total AUC increased to 0.94, along with an increase in the accuracy of the latter optimized models to 77.9 and 87%, respectively. CONCLUSION: The new intelligent models reliably estimated skeletal maturation and can be utilized in the clinical field or machine learning-based skeletal maturation assessment.

Skeletal maturation and the location of the mandibular foramen within the ramus mandibulae.

PURPOSE: It is well documented that the mandible does not grow at a constant rate. There are significant correlations between the increase of mandibular size and cervical vertebral maturation. The peak growth velocity of the mandible occurs after the third stage of cervical vertebral maturation. The location of the mandibular foramen (MF) and its changes subsequent to growth are of great interest to clinicians as they relate to the anesthesia of the inferior alveolar nerve and to mandibular surgical procedures. Therefore, the aim of the present study was to assess the influence of the mandibular growth spurt on the location of the MF in various skeletal growth patterns. METHODS: Panoramic and lateral cephalometric radiographs of 98 (32 orthognathic, 50 retrognathic, 16 prognathic) patients before and after the growth peak were collected. For each subject, the maturational stage of the cervical vertebrae was defined on successive lateral cephalograms and the vertical and horizontal position of the MF was evaluated on two panoramic radiographs, one before and one after the growth peak. RESULTS: The MF-Post/MF-Ant ratio (MF distance to the posterior border of the ramus/MF distance to the anterior border of the ramus) significantly increased after the growth peak in orthognathic and retrognathic subjects (P = 0.015 and 0.02, respectively). This ratio did not significantly increase in prognathic subjects (P = 0.882). No statistically significant changes in the vertical position of the MF were found in the three groups after the growth spurt. CONCLUSION: The horizontal position of the MF moves in an anterior direction in orthognathic and retrognathic subjects during the mandibular growth spurt. The vertical position of the mandibular foramen remains unchanged during this period.

Dentoskeletal effects of early class III treatment protocol based on timing of intervention in children.

BACKGROUND: To detect the optimal timing of intervention based on different cervical vertebral maturation stage (CS1-2 vs. CS3-4) for the treatment of Class III malocclusion with early Class III protocol. METHODS: A total sample of 43 patients (23 females, 20 males) ranging between 7 and 13 years of age with dentoskeletal Class III malocclusion treated with the modified SEC III (Splints, Elastic and Chincup) protocol divided into two groups based on the cervical vertebral maturation stages (CS1-2 and CS3-4) was included in this retrospective observational longitudinal study. Patient compliance was assessed using a 2-point Likert scale. Statistical comparisons between the two groups were performed with independent sample t tests. RESULTS: No statistically significant differences for any of the cephalometric variables describing the baseline dentoskeletal features were found between the two groups except for the mandibular unit length that was significantly greater in the pubertal group (P = 0.005). The modified SEC III protocol produced favorable sagittal outcomes in both groups, whereas no statistically significant T1-T2 changes were found between the CS1-2 and CS3-4 groups for any of the angular and linear measurements. No significant differences were found in the prevalence rates of the degree of collaboration between the two groups (P = 1.000). CONCLUSIONS: No significant differences between prepubertal and pubertal patients were found in the sagittal and vertical dentoskeletal changes with the modified SEC III protocol. Thus, this early Class III treatment produced similar favorable effects in growing subjects regardless of the cervical vertebral maturation stages from CS1 to CS4.

Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status.

BACKGROUND: Cervical vertebral maturation (CVM) is widely used to evaluate growth potential in the field of orthodontics. This study is aimed to develop an artificial intelligence (AI) system to automatically determine the CVM status and evaluate the AI performance. METHODS: A total of 1080 cephalometric radiographs, with the age of patients ranging from 6 to 22 years old, were included in the dataset (980 in training dataset and 100 in testing dataset). Two reference points and thirteen anatomical points were labelled and the cervical vertebral maturation staging (CS) was assessed by human examiners as gold standard. A convolutional neural network (CNN) model was built to train on 980 images and to test on 100 images. Statistical analysis was conducted to detect labelling differences between AI and human examiners, AI performance was also evaluated. RESULTS: The mean labelling error between human examiners was 0.48 ± 0.12 mm. The mean labelling error between AI and human examiners was 0.36 ± 0.09 mm. In general, the agreement between AI results and the gold standard was good, with the intraclass correlation coefficient (ICC) value being up to 98%. Moreover, the accuracy of CVM staging was 71%. In terms of F1 score, CS6 stage (85%) ranked the highest accuracy. CONCLUSIONS: In this study, AI showed a good agreement with human examiners, being a useful and reliable tool in assessing the cervical vertebral maturation.

Comparison of Deep Learning Models for Cervical Vertebral Maturation Stage Classification on Lateral Cephalometric Radiographs.

The purpose of this study is to evaluate and compare the performance of six state-of-the-art convolutional neural network (CNN)-based deep learning models for cervical vertebral maturation (CVM) on lateral cephalometric radiographs, and implement visualization of CVM classification for each model using gradient-weighted class activation map (Grad-CAM) technology. A total of 600 lateral cephalometric radiographs obtained from patients aged 6-19 years between 2013 and 2020 in Pusan National University Dental Hospital were used in this study. ResNet-18, MobileNet-v2, ResNet-50, ResNet-101, Inception-v3, and Inception-ResNet-v2 were tested to determine the optimal pre-trained network architecture. Multi-class classification metrics, accuracy, recall, precision, F1-score, and area under the curve (AUC) values from the receiver operating characteristic (ROC) curve were used to evaluate the performance of the models. All deep learning models demonstrated more than 90% accuracy, with Inception-ResNet-v2 performing the best, relatively. In addition, visualizing each deep learning model using Grad-CAM led to a primary focus on the cervical vertebrae and surrounding structures. The use of these deep learning models in clinical practice will facilitate dental practitioners in making accurate diagnoses and treatment plans.

Correlation between chronological age and skeletal maturity in different malocclusions: A retrospective study.

INTRODUCTION: The timing of initiation and duration of the pubertal peak is important in dentofacial orthopaedics as the mandible expresses maximum growth during the pubertal peak. The aim of the study was to compare the mean chronological age with skeletal age in three skeletal jaw relations. MATERIALS AND METHODS: The sample consisted of 787 cephalograms divided into class I, class II, and class III skeletal relationship, in which cervical vertebrae stages were assessed. Dunn test was used to compare different types of malocclusion. Pubertal peak duration was assessed by comparing the respective 95% CI. RESULTS: There was no statistically significant difference in the mean chronological age in all three malocclusion groups at CS2, CS3, CS4 and CS5. There was no significant difference in the mean age of boys and girls at each CVM stage, but class III boys have significantly delayed maturity during the pubertal spurt at CS4 (14.6 years±0.98) compared with girls (13.3 years±1.48) with P=0.02. The pubertal peak duration was 1.27 years (CI: 0.77-1.76) in the class I children, 0.96 (CI: 0.58-1.34) in the class II children and 1.15 years (CI: 0.26-2.0) in the class III children. In the class III children, the duration of the pubertal peak was higher for boys, 1.74 years (CI: 0.66-2.82) compared with girls, 0.8 years (CI: 0.42-2.82). CONCLUSIONS: There was positive correlation between chronological age and CVM stages in each skeletal jaw relation. Average time of attaining of a pubertal spurt is similar in class I, class II and class III children. Class III girls reach puberty slightly earlier than boys and puberty ends lately in class III boys. Class II boys have the shortest duration of the pubertal peak and class III boys have the longest duration of the pubertal peak.