Correlation assessment of cervical vertebrae maturation stage and mid-palatal suture maturation in an Iranian population

2020 ◽  
Vol 9 (3) ◽  
pp. 112-116
Author(s):  
Arezoo Mahdian ◽  
Yaser Safi ◽  
Kazem Dalaie ◽  
Shahab Kavousinejad ◽  
Mohammad Behnaz
2006 ◽  
Vol 76 (6) ◽  
pp. 984-989 ◽  
Author(s):  
Paola Gandini ◽  
Marta Mancini ◽  
Federico Andreani

Abstract Objective: To compare skeletal maturation as measured by hand-wrist bone analysis and by cervical vertebral analysis. Materials and Methods: A radiographic hand-wrist bone analysis and cephalometric cervical vertebral analysis of 30 patients (14 males and 16 females; 7–18 years of age) were examined. The hand-wrist bone analysis was evaluated by the Bjork index, whereas the cervical vertebral analysis was assessed by the cervical vertebral maturation stage (CVMS) method. To define vertebral stages, the analysis consisted of both cephalometric (13 points) and morphologic evaluation of three cervical vertebrae (concavity of second, third, and fourth vertebrae and shape of third and fourth vertebrae). These measurements were then compared with the hand-wrist bone analysis, and the results were statistically analyzed by the Cohen κ concordance index. The same procedure was repeated after 6 months and showed identical results. Results: The Cohen κ index obtained (mean ± SD) was 0.783 ± 0.098, which is in the significant range. The results show a concordance of 83.3%, considering that the estimated percentage for each case is 23.3%. The results also show a correlation of CVMS I with Bjork stages 1–3 (interval A), CVMS II with Bjork stage 4 (interval B), CVMS III with Bjork stage 5 (interval C), CVMS IV with Bjork stages 6 and 7 (interval D), and CVMS V with Bjork stages 8 and 9 (interval E). Conclusions: Vertebral analysis on a lateral cephalogram is as valid as the hand-wrist bone analysis with the advantage of reducing the radiation exposure of growing subjects.


2015 ◽  
Vol 4 (2) ◽  
pp. 23-26
Author(s):  
Sufia Nasrin Rita ◽  
SM Anwar Sadat

Class II malocclusion is the condition in which the mandibular first molars occlude distal to the normal relationship with the maxillary first molar. The etiology of class II malocclusion varied between skeletal, soft tissues, dental factors and habits. Skeletal class II could be because of protrusion of maxilla, retrusion of mandible and combination of both. The treatment modalities of any skeletal problem include Growth modification, Dental camouflage and Orthognathic surgery. The optimal time for treatment of patients with Class II malocclusions therapy should be initiated at the beginning of cervical vertebrae maturation stage CS3 to maximize the treatment effects. Age of treatment is approximately 8-14 years. The growth modification of moderate to severe skeletal class II malocclusion can be done by head gear, bionator, activator, twin block, herbest appliance, Frankel II regulator. The ultimate goal of growth modification depends on treatment timing, length of treatment, working mechanism of appliance, patient’s skeletal and dental condition we want to treat and the compliance of the patient.Update Dent. Coll. j: 2014; 4 (2): 23-26


2021 ◽  
Vol 10 (16) ◽  
pp. 3591
Author(s):  
Hyejun Seo ◽  
JaeJoon Hwang ◽  
Taesung Jeong ◽  
Jonghyun Shin

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.


2019 ◽  
Vol 4 (3) ◽  
pp. 149
Author(s):  
Wenti Komala ◽  
Endah Mardiati ◽  
Eky Soeria Soemantri ◽  
Isnaniah Malik

Cleft lip and palate is one of the most common congenital anomalies. Cleft lip and palate patients encounter growth problems in lip and palate area, although their overall growth and development remains unknown. Cervical vertebral maturation are indicators of physiological maturation used in interceptive treatment and orthognathic surgery. The present study aims to determine physiological maturation stage of cervical vertebrae maturation index in cleft andnon-cleft patients. Lateral cephalogram of 26 cleft patients and 27 non-cleft patients with a range of chronological age from 8-16 years old were involved. The cervical vertebrae maturation were analyzed in six stages of cervical vertebrae maturation method of Hassel and Farman. Data were analyzed using t-test (p≤ 0.05). The result shows that physiologicalmaturation stage of cervical vertebrae maturation index in cleft and non-cleft patients has no significant difference in stage acceleration (p= 0.38), stage transition (p= 0.41) and deceleration (p= 0.39). Likewise, there is no significant difference in physiological maturation stage of cervical vertebrae maturation index between cleft and non-cleft patients. 


2010 ◽  
Vol 24 (4) ◽  
pp. 433-437 ◽  
Author(s):  
Luci Mara Fachardo Jaqueira ◽  
Monica Costa Armond ◽  
Luciano José Pereira ◽  
Carlos Eduardo Pinto de Alcântara ◽  
Leandro Silva Marques

2021 ◽  
Vol 33 (1) ◽  
pp. 31
Author(s):  
Jeffri Vermilion ◽  
Mimi Marina Lubis

Pendahuluan: Periode tumbuh kembang pada perawatan pasien ortodonti merupakan hal penting untuk menentukan waktu perawatan maloklusi yang dapat dilihat dari maturasi skeletal. Perawatan kelas II skeletal paling baik dimulai pada masa pubertas atau cervical vertebrae maturation stage (CVMS) 3 atau 4 yaitu sekitar umur 10-12 tahun pada perempuan dan 12-14 pada laki-laki, dan pada kelas III pada masa prepubertal atau CVMS 1 yaitu sekitar 8-9 tahun untuk perempuan dan 8-11 tahun untuk laki-laki. Maturasi skeletal dapat dipengaruhi oleh status gizi seseorang. Tujuan penelitian untuk menganalisis perbedaan maturasi skeletal pada anak usia 8-12 tahun ditinjau berat badan dan jenis kelamin. Metode: Jenis penelitian observasional analitik yang dilakukan pada 100 pasien ortodonti RSGM USU usia 8-12 tahun terdiri dari 50 pasien berat badan kurang dan 50 pasien berat badan normal. Pasien berat badan kurang dan normal diperoleh melalui pengukuran berdasarkan indeks massa tubuh, kemudian dilakukan pengukuran maturasi skeletal menggunakan metode Bacetti yang terdiri dari CVMS 1-CVMS 6 dengan uji chi-square sebagai data analisis. Hasil: Maturasi skeletal berat badan kurang sebanyak 40% CVMS 1, 30% CVMS 2, 16% CVMS 3, 12% CVMS 4, dan 2% CVMS 5, sedangkan pada berat badan normal 12% CVMS 1, 34% CVMS 2, 26% CVMS 3, 18% CVMS 4, dan 10% CVMS 5. Hasil uji chi square menunjukkan terdapat perbedaan maturasi skeletal dengan berat badan kurang dan normal diperoleh nilai p=0,015; p<0,05 dan menunjukkan tidak terdapat perbedaan signifikan antara maturasi skeletal dengan jenis kelamin dimana p<0,05. Simpulan: Terdapat perbedaan maturasi skeletal antara berat badan kurang dan normal namun tidak terdapat perbedaan maturasi skeletal pada laki-laki dan perempuan pada anak usia 8-12 tahun.Kata kunci: Maturasi skeletal, indeks massa tubuh, metode Bacetti. ABSTRACTIntroduction: The growth and development period in orthodontic treatment is important in determining the malocclusion treatment timing, which can be seen from skeletal maturation. Class II skeletal treatment is best started at puberty or cervical vertebrae maturation stage (CVMS) 3 or 4, around the age of 10-12 years in women and 12-14 in men. In class III skeletal treatment is best started at the prepubertal period or CVMS 1, namely about 8-9 years for women and 8-11 years for men. Skeletal maturation can be affected by a person's nutritional status. This study was aimed to analyse the differences in skeletal maturation in children aged 8-12 years in terms of body weight and sex. Methods: This type of analytical observational study was conducted on 100 orthodontic patients at Universitas Sumatera Utara Dental Hospital aged 8-12 years consisting of 50 underweight patients and 50 normal-weight patients. The patients' weight was obtained through measurements based on body mass index; then, the skeletal maturation was measured using the Bacetti method consisting of CVMS 1-CVMS 6 with the chi-square test as data analysis. Results: Underweight skeletal maturation was 40% CVMS 1, 30% CVMS 2, 16% CVMS 3, 12% CVMS 4, and 2% CVMS 5, while at normal weight 12% CVMS 1, 34% CVMS 2, 26 % CVMS 3, 18% CVMS 4, and 10% CVMS 5. The chi square test results showed differences in skeletal maturation with underweight and normal body weight, the value of p=0.015; p<0.05 and no significant difference between skeletal maturation and sex where p<0.05. Conclusion: There is a difference in skeletal maturation between underweight and normal body weight, but there is no difference in skeletal maturation between sex in children aged 8-12 years.Keywords: Skeletal maturation, body mass index, Bacetti method.


2021 ◽  
Vol 45 (5) ◽  
pp. 352-358
Author(s):  
Francisco Guinot ◽  
Marina Ferrer ◽  
Lara Díaz-González ◽  
Cristina García ◽  
Isabel Maura

Aim: To evaluate the effects produced by functional orthodontic appliances at dental and skeletal level in relation to the level of skeletal maturation in class II patients. Study design: Longitudinal and observational study. Patients selected for the study had been wearing Sander Bite Jumping Appliance (SBJA) for at least 12 months; they were first diagnosed (T1) with skeletal class II according to Ricketts’ cephalometric analysis, and had had lateral cephalograms taken before and after orthopaedic treatment (T2). Variables studied at T1 and T2 were: facial convexity, inclination of the upper and lower incisors, and facial depth. Results were compared between T1 and T2 for each variable and in relation to cervical maturation stage (CVS) according to the Lamparski analysis. Statistical analysis was performed using Shapiro–Wilk, t-student, Analysis of Variance (ANOVA) and multiple comparison tests, taking as statistically significant a p-value &lt;0.05. Results: A final sample of 235 patients was obtained. Statistically significant differences were found in the inclination of the mandibular incisors between T1 and T2 and among the different cervical stages when the functional appliances were placed in CVS1 (p = 0.000), CVS2 (p = 0.04) or CVS5 (p = 0.048). For the remaining variables, significant differences were also found between T1 and T2, but these differences were similar in all cervical stages. Conclusions: A significant proclination of the mandibular incisors was found when the functional appliance was placed during CVS1, CVS2, or CVS5. Time of placement of the functional appliances was not statistically significant for the remaining variables studied.


2015 ◽  
Vol 86 (6) ◽  
pp. 955-961 ◽  
Author(s):  
Susan Rizk ◽  
Valmy Pangrazio Kulbersh ◽  
Riyad Al-Qawasmi

ABSTRACT Objective: To evaluate the effects of functional appliance treatment on the oropharyngeal airway volume, airway dimensions, and anteroposterior hyoid bone position of growing Class II patients. Materials and Methods: Twenty Class II white patients (mean age, 11.7 ± 1.75 years) treated with the MARA followed by fixed appliances were matched to an untreated control sample by cervical vertebrae maturation stage at pretreatment (T1) and posttreatment (T2) time points. Cone beam computed tomography scans were taken at T1 and T2. Dolphin3D imaging software was used to determine oropharyngeal airway volume, dimensions, and anteroposterior hyoid bone position. Results: Multivariate ANOVA was used to evaluate changes between T1 and T2. Oropharyngeal airway volume, airway dimensions, and A-P position of the hyoid bone increased significantly with functional appliance treatment. SNA and ANB decreased significantly in the experimental group (P ≤ .05). Changes in SNB and Sn-GoGn failed to reach statistical significance. Conclusions: Functional appliance therapy increases oropharyngeal airway volume, airway dimensions, and anteroposterior hyoid bone position in growing patients.


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