Evaluation of the Skeletal Maturation Using Lower First Premolar Mineralisation

Martha, Krisztina; Ogodescu, Alexandru; Gyergyay, Réka; Zsuzsa, Burján

doi:10.2478/amma-2013-0066

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…According to this study, correlation of mandibular 1st premolar and skeletal age is higher than the mandibular 2nd molar. This is the similar findings of Rózylo-Kalinowska et al 21 Valizadeh et al 30 and Krisztina et al 31 study. 32 …”

Section: Discussionsupporting

confidence: 90%

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Evaluation of Skeletal Maturation Using Mandibular 1st Premolar and 2nd Molar Calcification Stages: A Cross-sectional Radiographic Study

Sharma¹,

Mittal²,

Sharma³

et al. 2021

International Journal of Clinical Pediatric Dentistry

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A bstract Aim and objective The study aimed at, whether the calcification stages of mandibular 1st premolar and 2nd molar can be used to assess skeletal maturity. Materials and methods Lateral cephalogram and OPG of 72 growing subjects with good health (38 girls and 34 boys; age-group of 8–17 years) were selected for a cross-sectional study, in which the identity of patients was not revealed to the investigator. The dental calcification stage of mandibular 1st premolar and 2nd molar were estimated in accordance with the Demirjian system (DI) and skeletal maturation was estimated according to cervical vertebral maturation index (CVMI) given by Hassel and Farman and these two indices were compared. Results Chi-square test and Spearman rank-order correlation coefficient test showed that a significant relationship of mandibular 1st premolar and 2nd molar calcification stages with CVMI exists, for both males and females. In females, CVMI stage 2 showed maximum correlation to DI stage E of lower 2nd molar. In males, CVMI stage 2 showed maximum correlation to DI stage E of lower 1st premolar. Conclusion The result of this study revealed that each stage developed prior in female subjects compared to male subjects and the calcification stages of lower 1st premolar and 2nd molar can be used to assess the peak of growth. How to cite this article Mittal K, Trehan M, Sharma S, et al. Evaluation of Skeletal Maturation Using Mandibular 1st Premolar and 2nd Molar Calcification Stages: A Cross-sectional Radiographic Study. Int J Clin Pediatr Dent 2021;14(S-1):S44–S49.

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Section: Discussionsupporting

confidence: 90%

“…The mandibular premolar is also a precise indicator because the development of the mandibular 1st premolar begins at 21–24 months and ends at the age of 12–13 years. Rozylo-Kalinowska et al 21 reported that the calcification stages of mandibular premolars and canine shows the highest correlation with CVMI.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Skeletal Maturation Using Mandibular 1st Premolar and 2nd Molar Calcification Stages: A Cross-sectional Radiographic Study

Sharma¹,

Mittal²,

Sharma³

et al. 2021

International Journal of Clinical Pediatric Dentistry

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“…To estimate mandibular growth potential left, the patient must be localized on is growth curve, and many biologic indicators have been proposed: increase in body height, menarche, breast and voice changes, dental development and eruption, middle phalanx maturation of the third finger, maturation of the hand and wrist, and cervical vertebral maturation [3,[5][6][7][8]].…”

Section: Importance Of the Work And Its Interest For Orthodontics Commentioning

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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“…To estimate mandibular growth potential, the patient must be localized on is growth curve, and many biologic indicators have been proposed; increase in body height, menarche, breast and voice changes, dental development and eruption, middle phalanx maturation of the third finger, maturation of the wrist and cervical vertebral maturation [ 5 , 6 , 7 , 8 ].…”

Section: Importance Of the Work And Its Interest For The Orthodontmentioning

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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Evaluation of the Skeletal Maturation Using Lower First Premolar Mineralisation

Cited by 7 publications

References 19 publications

Evaluation of Skeletal Maturation Using Mandibular 1st Premolar and 2nd Molar Calcification Stages: A Cross-sectional Radiographic Study

Evaluation of Skeletal Maturation Using Mandibular 1st Premolar and 2nd Molar Calcification Stages: A Cross-sectional Radiographic Study

Determination of the Cervical Vertebra Maturation Degree from Lateral Radiography

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

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