Comparison of statural height growth velocity at different cervical vertebral maturation stages

Hosni, Sara; Burnside, Girvan; Watkinson, Simon; Harrison, Jayne E

doi:10.1016/j.ajodo.2017.12.016

Cited by 18 publications

(13 citation statements)

References 30 publications

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“…29 Results showed that the highest mean values were observed in stage III followed by stage IV, II, I and V. At CVMS III blood serum IGF-I levels were at peak with a signify value of 343.05ng/mL, and these peak levels can be linked with upcoming pubertal growth spurt, and residual mandibular growth. 15,28,20 Differences in results from other studies can be attributed to difference of chronological age for peak IGF-1 levels. Differences might also be due to the disparity in inclusion criteria, racial backgrounds, genetic factors, environmental factors, and the technique adopted for this study.…”

Section: Discussionmentioning

confidence: 95%

“…18,19 During the collection of blood samples, we ruled out the patients with bleeding disorders which is according to the study by Masoud et al 15 Cervical vertebral maturation techniques were established as the most reliable diagnostic tool. 20 It was useful in orthodontics as a marker for the status of skeletal maturity. 21 The CVMS method is proved more advantageous and beneficial as compared to hand-wrist method because of no additional radiation exposure.…”

Section: Discussionmentioning

confidence: 99%

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Correlation Between Serum IGF-1 Levels and CVM Stages for the Assessment of Skeletal Maturity

Sadiq¹,

Shamim²,

Azeem³

et al. 2020

J Pak Dent Assoc

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The major disadvantage in cervical vertebral maturation stages (CVMS) is the involvement of radiographic exposure. This study was undertaken to assess the applicability of insulin-like growth factor I (IGF-I) blood level as a maturation indicator by correlating it to the CVMS. METHODOLOGY: This cross-sectional study was conducted at orthodontic department of our institute. With 80% power of study, 5% desired level of significance and using 0.67 correlation value, a sample size of 75 was calculated. There are five stages of CVMS and in each CVM stage 15 subjects were allocated, therefore collective sample size was 75. The technique of sampling was purposive (non-probability) sampling. Out of 75 patients, 47 (62.7%) were males and 28 (37.3%) were females. The mean ages of the patients were 12.5 ± 2.6 years. Analysis of variance (ANOVA) test was performed to evaluate the blood serum IGF-1 levels among five stages of CVMS. RESULTS: There was a statistically considerable difference in mean IGF-1 among five stages of CVM. The mean IGF-1 (ng/ml) of CVMS 1 was 204.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Correlation Between Serum IGF-1 Levels and CVM Stages for the Assessment of Skeletal Maturity

Sadiq¹,

Shamim²,

Azeem³

et al. 2020

J Pak Dent Assoc

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show abstract

“…Many researchers found this method as valid and reliable as hand and wrist Xray [ 14 ]. The cervical vertebrae maturation stages have been demonstrated as a clinically useful maturation indicators for evaluation of pubertal growth height and mandibular velocities [ 18 , 19 , 20 ], by correlation between chronological age and cervical vertebrae maturation, between hand–wrist and cervical-vertebrae maturation [ 16 , 21 , 22 , 23 ].…”

Section: The Classical Radiographic Manual Methodsmentioning

confidence: 99%

Deep Learning and Artificial Intelligence for the Determination of the Cervical Vertebra Maturation Degree from Lateral Radiography

Makaremi

Lacaule

Mohammad-Djafari³

2019

Entropy

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Deep Learning (DL) and Artificial Intelligence (AI) tools have shown great success in different areas of medical diagnostics. In this paper, we show another success in orthodontics. In orthodontics, the right treatment timing of many actions and operations is crucial because many environmental and genetic conditions may modify jaw growth. The stage of growth is related to the Cervical Vertebra Maturation (CVM) degree. Thus, determining the CVM to determine the suitable timing of the treatment is important. In orthodontics, lateral X-ray radiography is used to determine it. Many classical methods need knowledge and time to look and identify some features. Nowadays, ML and AI tools are used for many medical and biological diagnostic imaging. This paper reports on the development of a Deep Learning (DL) Convolutional Neural Network (CNN) method to determine (directly from images) the degree of maturation of CVM classified in six degrees. The results show the performances of the proposed method in different contexts with different number of images for training, evaluation and testing and different pre-processing of these images. The proposed model and method are validated by cross validation. The implemented software is almost ready for use by orthodontists.

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“…Many researchers found this method as valid and reliable as hand and wrist Xray [14]. The cervical vertebrae maturation stages have been demonstrated as a clinically useful maturation indicators for evaluation of pubertal growth height and mandibular velocities [18][19][20], by correlation between chronological age and cervical vertebrae maturation, between hand-wrist and cervical-vertebrae maturation [16,[21][22][23].…”

Section: Cephalometric Appraisalsmentioning

confidence: 99%

Determination of the Cervical Vertebra Maturation Degree from Lateral Radiography

Makaremi

Lacaule

Mohammad-Djafari³

2020

The 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

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Many environmental and genetic conditions may modify jaws growth. In orthodontics, the right treatment timing is crucial. This timing is a function of the Cervical Vertebra Maturation (CVM) degree. Thus, determining the CVM is important. In orthodontics, the lateral X-ray radiography is used to determine it. Many classical methods need knowledge and time to look and identify some features to do it. Nowadays, Machine Learning (ML) and Artificial Intelligent (AI) tools are used for many medical and biological image processing, clustering and classification. This paper reports on the development of a Deep Learning (DL) method to determine directly from the images the degree of maturation of CVM classified in six degrees. Using 300 such images for training and 200 for evaluating and 100 for testing, we could obtain a 90% accuracy. The proposed model and method are validated by cross validation. The implemented software is ready for use by orthodontists.

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Comparison of statural height growth velocity at different cervical vertebral maturation stages

Cited by 18 publications

References 30 publications

Correlation Between Serum IGF-1 Levels and CVM Stages for the Assessment of Skeletal Maturity

Correlation Between Serum IGF-1 Levels and CVM Stages for the Assessment of Skeletal Maturity

Deep Learning and Artificial Intelligence for the Determination of the Cervical Vertebra Maturation Degree from Lateral Radiography

Determination of the Cervical Vertebra Maturation Degree from Lateral Radiography

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