Estimating mandibular growth stage based on cervical vertebral maturation in lateral cephalometric radiographs using artificial intelligence.

INTRODUCTION: Determining the right time for orthodontic treatment is one of the most important factors affecting the treatment plan and its outcome. The aim of this study is to estimate the mandibular growth stage based on cervical vertebral maturation (CVM) in lateral cephalometric radiographs using artificial intelligence. Unlike previous studies, which use conventional CVM stage naming, our proposed method directly correlates cervical vertebrae with mandibular growth slope. METHODS AND MATERIALS: To conduct this study, first, information of people achieved in American Association of Orthodontics Foundation (AAOF) growth centers was assessed and after considering the entry and exit criteria, a total of 200 people, 108 women and 92 men, were included in the study. Then, the length of the mandible in the lateral cephalometric radiographs that were taken serially from the patients was calculated. The corresponding graphs were labeled based on the growth rate of the mandible in 3 stages; before the growth peak of puberty (pre-pubertal), during the growth peak of puberty (pubertal) and after the growth peak of puberty (post-pubertal). A total of 663 images were selected for evaluation using artificial intelligence. These images were evaluated with different deep learning-based artificial intelligence models considering the diagnostic measures of sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). We also employed weighted kappa statistics. RESULTS: In the diagnosis of pre-pubertal stage, the convolutional neural network (CNN) designed for this study has the higher sensitivity and NPV (0.84, 0.91 respectively) compared to ResNet-18 model. The ResNet-18 model had better performance in other diagnostic measures of the pre-pubertal stage and all measures in the pubertal and post-pubertal stages. The highest overall diagnostic accuracy was also obtained using ResNet-18 model with the amount of 87.5% compared to 81% in designed CNN. CONCLUSION: The artificial intelligence model trained in this study can receive images of cervical vertebrae and predict mandibular growth status by classifying it into one of three groups; before the growth spurt (pre-pubertal), during the growth spurt (pubertal), and after the growth spurt (post-pubertal). The highest accuracy is in post-pubertal stage with the designed networks.

Is hand-wrist radiography still necessary in orthodontic treatment planning?

OBJECTIVES: The aim of our study is to compare the relationship between hand-wrist and cervical vertebra maturation stages with chronological age and to investigate the effect of malocclusion type on the relationship between these methods. MATERIALS AND METHODS: Hand-wrist and cephalometric radiographs of 1000 patients (526 females, 474 males) with a mean age of 13.41 ± 1.83 were analyzed. The methods of Bacetti et al. were used for the cervical vertebra maturation stage, and Björk, Grave and Brown's methods were used for the hand-wrist maturation stage. One-way ANOVA test was applied to compare skeletal classes between them. Tukey post hoc test was used to determine the differences. The relationship between the malocclusion type, cervical vertebra and hand-wrist maturation stages was evaluated with the Spearman correlation test. RESULTS: Spearman's correlation coefficient was 0.831, 0.831 and 0.760 in Class I, II and III females, respectively. In males, it was calculated as 0.844, 0.889 and 0.906, respectively. When sex and malocclusion were not differentiated, the correlation was found to be 0.887. All were statistically significant (P < 0.001). The highest correlation was observed in class III males, while the lowest was found in class III females. CONCLUSION: Cervical vertebrae can be used safely to assess pubertal spurt without hand-wrist radiography. Diagnosing growth and development stages from cephalometric images is important in reducing additional workload and preventing radiation risk.

Influence of BMI percentile on craniofacial morphology and development in adolescents,Part II: elevated BMI is associated with larger final facial dimensions.

BACKGROUND: Prevalence of adolescent obesity has markedly increased from 5.2% in 1974 to 19.7% in 2021. Understanding the impacts of obesity is important to orthodontists, as growth acceleration and greater pre-pubertal facial dimensions are seen in children with elevated body mass index (BMI). METHODS: To identify whether adolescent obesity shifts the timing and rate of craniofacial growth resulting in larger post-treatment dimensions, we evaluated cephalometric outcomes in overweight/obese (BMI > 85%, n = 168) and normal weight (n = 158) adolescents (N = 326 total). Cephalometric measurements were obtained from pre- and post-treatment records to measure growth rates and final dimensions and were statistically evaluated with repeated measures analysis of variance and linear regression models. RESULTS: Overweight and obese adolescents began and finished treatment with significantly larger, bimaxillary prognathic craniofacial dimensions, with elevated mandibular length [articulare-gnathion (Ar-Gn)], maxillary length [condylion-anterior nasal spine (Co-ANS), posterior nasal spine-ANS (PNS-ANS)], and anterior lower face height (ANS-Me), suggesting overweight children grow more overall. However, there was no difference between weight cohorts in the amount of cephalometric change during treatment, and regression analyses demonstrated no correlation between change in growth during treatment and BMI. BMI percentile was a significant linear predictor (P < 0.05) for cephalometric post-treatment outcomes, including Ar-Gn, Co-ANS, ANS-Me, upper face height percentage (UFH:total FH, inverse relationship), lower face height percentage (LFH:total FH), sella-nasion-A-point (SNA), and SN-B-point (SNB). LIMITATIONS: The study is retrospective. CONCLUSIONS: Growth begins earlier in overweight and obese adolescents and continues at a rate similar to normal-weight children during orthodontic treatment, resulting in larger final skeletal dimensions. Orthodontics could begin earlier in overweight patients to time care with growth, and clinicians can anticipate that overweight/obese patients will finish treatment with proportionally larger, bimaxillary-prognathic craniofacial dimensions.

Comparison between Two Methods of Skeletal Growth Evaluation: Cervical Vertebrae Maturations and Middle Phalanx Maturation.

Aim: Growth measurement has always been essential to identify the best time to employ orthopedic or orthodontic appliances. Optimal timing for orthodontic treatment is strictly linked to the identification of periods of craniofacial growth when treatment is more effective.The aim of this study was to compare two different methods, middle phalanx maturation (MPM) and cervical vertebrae maturation (CVM), used to evaluate the stage of facial growth. Materials and methods: The research data was collected from July 2018 to April 2019 at the Dental Clinic of the San Gerardo Hospital in Monza. The study included a sample of 98 patients-46 males and 52 females. For each patient, a latero-lateral teleradiography of the skull and an X-ray on the middle finger of the right hand were obtained.The statistical analysis of the comparison of the stages of skeletal maturation obtained by the MPM and CVM methods was performed using the correlation coefficient for ranks of Spearman. Results: A descriptive statistical analysis of the entire sample of 98 patients was performed (mean age of 12.2 years and median of 12.2 years). The average age of females in every single stage of MPM was significantly lower than the average age of males. Of the total sample, 87 patients (88.8%) showed complete agreement between the two methods. Conclusion: The results obtained from the statistical analysis of this study allowed us to confirm a satisfactory agreement between the two methods.The intermediate phalanx method is a valid and alternative indicator to CVM for the identification of the puberty growth peak. We can, therefore, consider the MPM method a valid indicator of skeletal maturity. How to cite this article: Mirabelli L, Bianco E, Pigato G, et al. Comparison between Two Methods of Skeletal Growth Evaluation: Cervical Vertebrae Maturations and Middle Phalanx Maturation. Int J Clin Pediatr Dent 2023;16(2):327-332.

AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages.

OBJECTIVE: A study of supervised automated classification of the cervical vertebrae maturation (CVM) stages using deep learning (DL) network is presented. A parallel structured deep convolutional neural network (CNN) with a pre-processing layer that takes X-ray images and the age as the input is proposed. METHODS: A total of 1018 cephalometric radiographs were labelled and classified according to the CVM stages. The images were separated according to gender for better model-fitting. The images were cropped to extract the cervical vertebrae automatically using an object detector. The resulting images and the age inputs were used to train the proposed DL model: AggregateNet with a set of tunable directional edge enhancers. After the features of the images were extracted, the age input was concatenated to the output feature vector. To have the parallel network not overfit, data augmentation was used. The performance of our CNN model was compared with other DL models, ResNet20, Xception, MobileNetV2 and custom-designed CNN model with the directional filters. RESULTS: The proposed innovative model that uses a parallel structured network preceded with a pre-processing layer of edge enhancement filters achieved a validation accuracy of 82.35% in CVM stage classification on female subjects, 75.0% in CVM stage classification on male subjects, exceeding the accuracy achieved with the other DL models investigated. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If AggregateNet is used without directional filters, the test accuracy decreases to 80.0% on female subjects and to 74.03% on male subjects. CONCLUSION: AggregateNet together with the tunable directional edge filters is observed to produce higher accuracy than the other models that we investigated in the fully automated determination of the CVM stages.

Deep convolutional neural network-the evaluation of cervical vertebrae maturation.

OBJECTIVES: This study aimed to automatically determine the cervical vertebral maturation (CVM) processes on lateral cephalometric radiograph images using a proposed deep learning-based convolutional neural network (CNN) model and to test the success rate of this CNN model in detecting CVM stages using precision, recall, and F1-score. METHODS: A total of 588 digital lateral cephalometric radiographs of patients with a chronological age between 8 and 22 years were included in this study. CVM evaluation was carried out by two dentomaxillofacial radiologists. CVM stages in the images were divided into 6 subgroups according to the growth process. A convolutional neural network (CNN) model was developed in this study. Experimental studies for the developed model were carried out in the Jupyter Notebook environment using the Python programming language, the Keras, and TensorFlow libraries. RESULTS: As a result of the training that lasted 40 epochs, 58% training and 57% test accuracy were obtained. The model obtained results that were very close to the training on the test data. On the other hand, it was determined that the model showed the highest success in terms of precision and F1-score in the CVM Stage 1 and the highest success in the recall value in the CVM Stage 2. CONCLUSION: The experimental results have shown that the developed model achieved moderate success and it reached a classification accuracy of 58.66% in CVM stage classification.

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

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

Skeletal maturation in different anteroposterior and vertical skeletal growth patterns in female subjects.

PURPOSE: The aim of the present work was to study the sequence of skeletal maturation in the various anteroposterior and vertical skeletal growth patterns and to detect whether differences existed between them. METHODS: Cephalograms of 861 growing and adolescent female patients were traced to categorize the subjects into 9 skeletal patterns. Each subject was assigned a skeletal maturational stage. Analysis of variance (ANOVA) followed by Bonferroni test were used to detect differences in the onset of the three growth stages (prepubertal, pubertal and postpubertal) between the 9 groups. The same statistical methods were used to detect differences between the mean ages at the three growth stages within each group. RESULTS: No statistically significant differences were found between the mean ages of pubertal and postpubertal growth stages between the 9 skeletal patterns. However, class III growers had a significantly earlier onset of prepubertal growth (10.25 ± 1.56 years) when compared to that of class II high angle cases (11.11 ± 1.67 years; P < 0.01). Also, significant differences were found between the mean ages at the three growth stages within the groups. CONCLUSION: A map was created defining the sequence of skeletal maturation for each skeletal growth pattern. This map defines clinically relevant differences in the starting time points and the optimum intervals of growth modification for each skeletal growth pattern.

Assessment of dehydroepiandrosterone sulphate (DHEAS) and cortisol levels in saliva and their correlation to cervical vertebrae maturation method in males and females at different growth stages: a clinical study

ABSTRACT Objective: The aim of this in-vivo study was to assess the salivary dehydroepiandrosterone sulphate (DHEAS) and cortisol levels, and their correlation to the Cervical Vertebrae Maturation method (CVM) in males and females at pre-pubertal, pubertal and post-pubertal growth stages. Methods: 48 patients (24 males, 24 females) who were to undergo routine orthodontic treatment were screened according to the inclusion and exclusion criteria. Then subjects were grouped according to CVM stages, using lateral cephalogram, in pre-pubertal, pubertal and post-pubertal groups. Unstimulated saliva from the selected subjects was collected. DHEAS and cortisol levels in the salivary samples were estimated by Enzyme-Linked Immunosorbent assay (ELISA). Then they were compared to Cervical Vertebrae Maturation Method stages. One-way ANOVA test followed by Tukey's post-hoc test was used to compare the salivary DHEAS and cortisol levels between different CVM stages in males and females. Independent Student t-test was used to compare the mean salivary DHEAS and cortisol levels between different males and females in each CVM stage. Result: There was a progressive increase in salivary DHEAS and cortisol concentration as skeletal maturation progressed from CVM stages 1 and 2, CVM stages 3 and 4, reaching the highest value at CVM stages 5 and 6. Their levels were higher in males than females. Conclusion: The salivary DHEAS and cortisol levels can be useful as a potential indicator of skeletal maturation, to aid in the assessment of pubertal status.

RESUMO Objetivo: O objetivo deste estudo in vivo foi avaliar os níveis salivares de sulfato de dehidroepiandrosterona (DHEAS) e de cortisol, e sua correlação com o método de maturação das vértebras cervicais (CVM) em homens e mulheres nas fases de crescimento pré-puberal, puberal e pós-puberal. Métodos: 48 pacientes (24 homens, 24 mulheres) que se submeteriam a tratamento ortodôntico de rotina foram selecionados de acordo com os critérios de inclusão e exclusão. Em seguida, usando telerradiografia lateral, os indivíduos foram agrupados de acordo com os estágios CVM, em grupos pré-puberal, puberal e pós-puberal. Foi feita coleta de saliva não estimulada nos indivíduos selecionados. Os níveis de DHEAS e cortisol nas amostras salivares foram avaliados pelo teste ELISA (Enzyme-Linked Immunosorbent assay). Em seguida, foram comparados aos estágios do método CVM. O teste ANOVA de uma via seguido pelo teste post-hoc de Tukey foi usado para comparar o DHEAS salivar e os níveis de cortisol entre os diferentes estágios de CVM em homens e mulheres. O teste t de Student independente foi usado para comparar a média de DHEAS salivar e os níveis de cortisol entre diferentes homens e mulheres em cada estágio de CVM. Resultados: Houve um aumento progressivo no DHEAS salivar e na concentração de cortisol à medida que a maturação esquelética progrediu dos estágios CVM 1 e 2, para os estágios CVM 3 e 4, atingindo o valor mais alto nos estágios CVM 5 e 6. Seus níveis foram maiores nos homens do que nas mulheres. Conclusões: Os níveis salivares de DHEAS e cortisol podem ser úteis como um potencial indicador de maturação esquelética, para auxiliar na avaliação do estado puberal.

Neural networks for classification of cervical vertebrae maturation: a systematic review.

OBJECTIVE: To assess the accuracy of identification and/or classification of the stage of cervical vertebrae maturity on lateral cephalograms by neural networks as compared with the ground truth determined by human observers. MATERIALS AND METHODS: Search results from four electronic databases (PubMed [MEDLINE], Embase, Scopus, and Web of Science) were screened by two independent reviewers, and potentially relevant articles were chosen for full-text evaluation. Articles that fulfilled the inclusion criteria were selected for data extraction and methodologic assessment by the QUADAS-2 tool. RESULTS: The search identified 425 articles across the databases, from which 8 were selected for inclusion. Most publications concerned the development of the models with different input features. Performance of the systems was evaluated against the classifications performed by human observers. The accuracy of the models on the test data ranged from 50% to more than 90%. There were concerns in all studies regarding the risk of bias in the index test and the reference standards. Studies that compared models with other algorithms in machine learning showed better results using neural networks. CONCLUSIONS: Neural networks can detect and classify cervical vertebrae maturation stages on lateral cephalograms. However, further studies need to develop robust models using appropriate reference standards that can be generalized to external data.

Skeletal Maturation in Patients With Cleft Lip and/or Palate: A Systematic Review.

OBJECTIVE: The objective of this systematic review was to evaluate the evidence regarding skeletal maturation in patients with cleft lip and/or palate (CL/P) and to investigate whether the skeletal maturation is delayed in these patients. DESIGN: Systematic review. METHODS: Electronic and manual searches of scientific literature were conducted in 4 databases (MEDLINE, Embase, Cochrane Library, and Web of Science). Cohort studies that compared the skeletal maturation of patients with CL/P with that of children without CL/P were eligible for inclusion. The quality of included cohort studies was assessed using the Newcastle-Ottawa Scale. PATIENTS AND PARTICIPANTS: Patients of any sex and ethnicity with CL/P and children without CL/P were included in this systematic review. MAIN OUTCOME MEASURES: Difference in skeletal maturation between patients with CL/P and patients without CL/P. RESULTS: Thirteen retrospective cohort studies were included in this systematic review. Ten studies were considered of high quality and 3 were considered of general quality. The results of the included studies comparing skeletal maturation of patients with CL/P and children without CL/P were heterogeneous. CONCLUSION: Heterogeneity of skeletal maturation assessment methods, chronological age, sex, cleft type, and race may influence the final results of clinical studies on skeletal maturation in patients with CL/P. Overall, there is limited evidence to determine whether the skeletal maturation level of patients with CL/P is delayed compared to that of normal children. Further studies are needed to determine the skeletal maturation patterns in patients with CL/P.

Skeletal maturation pattern among Down Syndrome individuals in Benin, City. Nigeria

Background: The assessment of skeletal maturity is important in the timing of orthodontic treatment especially in the modification of dento-facial growth. The use of cervical vertebrae as a method of assessment of skeletal maturity has rarely been used among Down Syndrome. Objective: To assess skeletal maturity among individuals with Down Syndrome using the cervical vertebrae maturation stages. Methods: The study was conducted among 21 Down Syndrome with mean ages of 11.70  1.83 years (males) and 13.64  1.75 years (female); and 21 control individuals with mean ages of 12.00  2.00 years (male), and 13.50  1.90 years (female). The independent t-test and chi-square test were used to determine significant differences among the continuous (age) and categorical variables (cervical vertebrae maturation stages) respectively when matched with gender and chronological age. Fischer exact test was used when an expected frequency presentation was <5. A p-value of < 0.05 was set as statistically significant. Results: Down Syndrome males had delayed maturation at 11 years but accelerated at 12 with early attainment of maturity at 15 years. Down Syndrome female had a delay tendency in skeletal maturation from 11­15 years of age. Overall, Down Syndrome had a 1.242 probability of either having a delay or advancement in skeletal maturation which was not statistically significant. Conclusively, the skeletal maturation pattern between Down syndrome patients and normal individuals was not statistically different. Conclusion: The average timing for commencement of orthodontic treatment especially growth modification for normal individuals can be applied for individuals with Down Syndrome as this present study did not show any statistically significant difference in their overall skeletal maturation.

Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network.

Recently, the estimation of bone maturation using deep learning has been actively conducted. However, many studies have considered hand-wrist radiographs, while a few studies have focused on estimating cervical vertebral maturation (CVM) using lateral cephalograms. This study proposes the use of deep learning models for estimating CVM from lateral cephalograms. As the second, third, and fourth cervical vertebral regions (denoted as C2, C3, and C4, respectively) are considerably smaller than the whole image, we propose a stepwise segmentation-based model that focuses on the C2-C4 regions. We propose three convolutional neural network-based classification models: a one-step model with only CVM classification, a two-step model with region of interest (ROI) detection and CVM classification, and a three-step model with ROI detection, cervical segmentation, and CVM classification. Our dataset contains 600 lateral cephalogram images, comprising six classes with 100 images each. The three-step segmentation-based model produced the best accuracy (62.5%) compared to the models that were not segmentation-based.

Correlation and Correspondence between Skeletal Maturation Indicators in Hand-Wrist and Cervical Vertebra Analyses and Skeletal Maturity Score in Korean Adolescents.

This retrospective observational study aimed to examine the correlation and correspondence between skeletal maturation indicators (SMI), cervical vertebral maturation indicators (CVMI), and radius-ulna-short bones (RUS) skeletal maturity scores in Korean adolescents, and to determine whether easily obtainable SMI or CVMI can replace the RUS skeletal maturity score. A total of 1017 participants were included with both hand-wrist radiograph and lateral cephalogram acquired concurrently. From the lateral cephalogram, CVMI was determined; through the hand-wrist radiograph, SMI was categorized, and the RUS skeletal maturity score was evaluated as well. Associations were examined using the Mann-Whitney U test, Spearman's rank-order correlation analysis, and multiple correspondence analysis. There was no statistically significant difference in chronological age between males and females; however, the SMI, CVMI, and RUS skeletal maturity scores were significantly higher in females. The SMI, CVMI, and RUS skeletal maturity scores showed a statistically significant strong degree of both positive correlation and correspondence. However, a precisely corresponding RUS skeletal maturity score was difficult to obtain for a specific CVMI and SMI stage, implying the absence of a quantitative correlation. In conclusion, detailed evaluation should be conducted using the RUS skeletal maturity score, preferably in cases that require bone age determination or residual growth estimation.

Prediction of hand-wrist maturation stages based on cervical vertebrae images using artificial intelligence.

OBJECTIVE: To predict the hand-wrist maturation stages based on the cervical vertebrae (CV) images, and to analyse the accuracy of the proposed algorithms. SETTINGS AND POPULATION: A total of 499 pairs of hand-wrist radiographs and lateral cephalograms of 455 orthodontic patients aged 6-18 years were used for developing the prediction model for hand-wrist skeletal maturation stages. MATERIALS AND METHODS: The hand-wrist radiographs and the lateral cephalograms were collected from two university hospitals and a paediatric dental clinic. After identifying the 13 anatomic landmarks of the CV, the width-height ratio, width-perpendicular height ratio and concavity ratio of the CV were used as the morphometric features of the CV. Patients' chronological age and sex were also included as input data. The ground truth data were the Fishman SMI based on the hand-wrist radiographs. Three specialists determined the ground truth SMI. An ensemble machine learning methods were used to predict the Fishman SMI. Five-fold cross-validation was performed. The mean absolute error (MAE), round MAE and root mean square error (RMSE) values were used to assess the performance of the final ensemble model. RESULTS: The final ensemble model consisted of eight machine learning models. The MAE, round MAE and RMSE were 0.90, 0.87 and 1.20, respectively. CONCLUSION: Prediction of hand-wrist SMI based on CV images is possible using machine learning methods. Chronological age and sex increased the prediction accuracy. An automated diagnosis of the skeletal maturation may aid as a decision-supporting tool for evaluating the optimal treatment timing for growing patients.

Effect of positional errors on the accuracy of cervical vertebrae maturation assessment using CBCT and lateral cephalograms.

BACKGROUND: The objective of this study was to evaluate the effects of single plane and multiplane rotational errors in yaw, pitch, and roll of the head while recording the lateral cephalogram on CVM (cervical vertebrae maturity) assessment. METHODS: A total of 40 cone-beam computed tomography (CBCT) scans and 360 lateral cephalograms were analyzed for patients with different rotations: Controls (no rotation), Y5 (yaw 5° rotation), Y10 (yaw 10° rotation), R5 (roll 5° rotation), R10 (Roll 10° rotation), P5 (pitch 5° rotation), P10 (pitch 10° rotation), YRP5 (yaw, roll, and pitch 5° rotation), and YRP10 (yaw, roll, and pitch 10° rotation). The C2, C3, and C4 concavity and their base-anterior ratio and posterior-anterior ratio were measured. In addition, maxillomandibular linear parameters, such as effective mandibular length and height, mandibular body length, effective midface length, and maxillomandibular differential, were also evaluated. RESULTS: Y5, Y10, R5, and R10 led to overestimation of CVM in comparison with controls. Multiplane rotations (YRP5 and YRP10) led to more inaccuracies in CVM measurements than single plane rotations; 10° of rotation led to more inaccuracies than 5° of rotation while recording the lateral cephalogram, irrespective of the plane. Yaw rotational errors led to an underestimation of maxillomandibular linear measurements, whereas roll rotational errors led to an overestimation of the measurements; however, there were wide individual variations in the measurements between the different rotations and controls. CONCLUSIONS: Rotational errors lead to overestimation of CVM assessment. Multiplane rotations cause higher inaccuracies than single plane rotations. Increased degree of rotations while capturing the lateral cephalograms lead to more inaccuracies in CVM assessment.

Correlation between skeletal development and maxillary canine eruption.

AIM: The aim of this study was to find a correlation between the permanent maxillary canine eruption and the cervical vertebral maturation index (CVMI). MATERIALS AND METHODS: 145 subjects (73 male and 72 female) in the age of 7-14 years were examined radiographically with lateral cephalographs and orthopantomographs. The CVM patterns were evaluated on the lateral cephalograph using the classification of Hassel and Farman. The stage of the permanent maxillary canine eruption has been investigated on the orthopantomographs depending on its vertical height in relation to the adjacent incisor. Descriptive statistics were obtained for both CVMI stages and canine eruption grading. Spearman rank correlation test was used to determine the correlation between both methods. The minimum level of significance was considered less than 0.05 (P < 0.05). RESULTS: Results showed a strong correlation between CVMI and the grading of the maxillary canine eruption in both female and male and the (r) value estimated was 0.862 and 0.758, respectively. Over 90% of deceleration stage of CVMI in both genders show canine eruption (pubertal growth spurt) about 91.66% for female and 95.65% for male and a small percentage of delay eruption 8.33% and 4.35% in female and male gender, respectively, with a predilection to the female gender. CONCLUSIONS: A significant correlation between the permanent maxillary canine eruption stages and skeletal maturity was found. The eruption of maxillary canine occurs before the end of pubertal growth. Any delay in the eruption of maxillary canine after the deceleration stage of CVMI, suggesting a chance of impaction.

Reliability of cephalograms derived of cone beam computed tomography versus lateral cephalograms to estimate cervical vertebrae maturity in a Peruvian population: A retrospective study.

OBJECTIVE: Cone beam computed tomography (CBCT) images can be useful for estimating cervical vertebrae maturity (CVM). The aim of this study was to evaluate the reliability of cephalograms derived from CBCT versus lateral cephalograms (LC) to estimate the CVM in a Peruvian population. MATERIAL AND METHODS: The sample evaluated consisted of 40 cephalograms derived from CBCT and 40 LC images from individuals aged 10-19 years. One trained and calibrated observer (Kappa scores≥0.90) interpreted the CBCT and LC images twice. Intra-observer reliability of each maturation stage on CBCT sagittal slices and LC images were analysed using the weighted kappa statistics (α=0.05). Comparison of CVM stages between CBCT slices and LC images were analysed by the Spearman rank correlation coefficient, p<0.05. RESULTS: The weighted kappa test showed almost perfect intra-observer agreement for the CVM stages using the CBCT sagittal slices (0.873). Considering the LC images, the weighted kappa test showed almost perfect intra-observer agreement too (0.937). In both intra-observer agreement, the difference was limited to one maturation stage of the CVM method. The first and second intra-observer agreement for the CVM stages between the CBCT sagittal slices and LC images were almost perfect (0.937 and 0.874). High correlation values at the first (0.975) and second (0.976) intra-observer agreement for the CVM stages between CBCT sagittal slices and LC images were also found. CONCLUSION: CBCT is a reliable method for CVM assessment and can be used as an alternative method for this purpose. The orthodontists might use the CBCT scans as a valuable tool for CVM method estimation.

Estimation and Comparison of the Duration of the Pubertal Peak in Skeletal Class II and Class I Subjects Using the Cervical Vertebrae Maturation Index Method.

AIM: The purpose of this study is to estimate and compare the duration of the pubertal peak in skeletal class II and class I subjects and to detect any difference between boys and girls or between hypo-, normo-, and hyperdivergent subjects for skeletal maturation indicator (CVM) in white Caucasians. MATERIALS AND METHODS: 346 subjects were selected from 3,119 examined files. Pretreatment lateral cephalometric records were hand-traced and divided following the anteroposterior skeletal relationship, the gender, the vertical pattern, and the skeletal maturation. The duration of the pubertal peak was calculated based on the chronological age interval according to each group. The age of onset of the active growth and the duration of the pubertal peak were compared between the different groups studied. RESULTS: Pubertal peak had a mean duration of 13 months in skeletal class I subjects, 19 months in skeletal class II subjects, 15 months in girls, 20 months in boys, 13 months in normodivergent and hypodivergent subjects, whereas in hyperdivergent subjects, it lasted 18 months. CONCLUSION: The growth interval corresponding to the pubertal growth spurt (CS3-CS4) was (1) significant between skeletal class I and class II subjects, (2) longer in boys, and (3) longer in hyperdivergent subjects. CLINICAL SIGNIFICANCE: Orthodontic treatments can start earlier for girls in class I or class II relationship and for hyperdivergent subjects as well. Furthermore, boys and subjects in class II skeletal relationship have a significantly longer duration of the pubertal peak and consequently a much efficient orthopedic and orthodontic treatment.

A Study of Correlation of Various Growth Indicators with Chronological Age.

INTRODUCTION: The aim of this study was to assess the relationship of chronological age with cervical vertebrae skeletal maturation, frontal sinus width and antegonial notch depth and a correlation, if any, among the three variables. MATERIALS AND METHODS: The samples were derived from lateral cephalometric radiographs of 80 subjects (40 males, 40 females; age range: 10 to 19 years). Cervical vertebral development was evaluated by the method of Hassel and Farman, frontal sinus width was measured by the method described by Ertürk and antegonial notch depth as described by Singer et al. The Pearson's correlation coefficients were estimated to assess the relationship of chronological age with cervical vertebrae skeletal maturation, frontal sinus width and antegonial notch depth. RESULTS: The Pearson's correlation coefficient were 0.855 (p < 0.001) between chronological age and cervical vertebrae skeletal maturation, and 0.333 (p < 0.001) between chronological age and frontal sinus width. CONCLUSION: A highly significant positive correlation was found between chronological age and cervical vertebrae skeletal maturation, and between chronological age and frontal sinus width. Nonsignificant correlation was found between chronological age and antegonial notch depth. How to cite this article: Singh S, Sandhu N, Puri T, Gulati R, Kashyap R. A Study of Correlation of Various Growth Indicators with Chronological Age. Int J Clin Pediatr Dent 2015;8(3): 190-195.