Highlights
- •This study aimed to develop a deep convolutional neural network (CNN) for automatic classification of pubertal growth spurts using cervical vertebral maturation staging based on the lateral cephalograms of Iranian subpopulation.
- •The study findings confirm the potential of convolutional neural networks as a diagnostic tool for intelligent evaluation of skeletal maturation with high accuracy even with a relatively small number of images.
ABSTRACT
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.
Keywords
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References
- An improved version of the cervical vertebral maturation (CVM) method for the assessment of mandibular growth.Angle Orthod. 2002; 72: 316-323
- Reliability of cervical vertebral maturation compared to hand-wrist for skeletal maturation assessment in growing subjects: a systematic review.J Back Musculoskelet Rehabil. 2021; 34: 925-936
- Is the cervical vertebral maturation (CVM) method effective enough to replace the hand-wrist maturation (HWM) method in determining skeletal maturation? A systematic review.Eur J Radiol. 2018; 102: 125-128
- Reliability of the cervical vertebrae maturation (CVM) method.Bratisl Lek Listy. 2015; 116: 222-226
- Reliability of cervical vertebral maturation staging.Am J Orthod Dentofacial Orthop. 2016; 150: 98-104
- Validity and reliability of a method for assessment of cervical vertebral maturation.Angle Orthod. 2012; 82: 229-234
- Mandibular growth changes and maturation of cervical vertebrae — a longitudinal cephalometric study.Angle Orthod. 1988; 58: 179-184
- Current perspectives in medical image perception.Atten Percept Psychophys. 2010; 72: 1205-1217
- Assessment of skeletal and dental maturity levels for a given chronological age among Indian children.J Contemp Dent Pract. 2012; 13: 310-315
- Deep learning.MIT press, Cambridge2016
- Sex determination from lateral cephalometric radiographs using an automated deep learning convolutional neural network.Imaging Sci Dent. 2022; 52: 239-244
- Deep convolutional neural networks for chest diseases detection.J Healthc Eng. 2018; 20184168538
- Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm.J Dent. 2018; 77: 106-111
- Understanding the mechanisms of deep transfer learning for medical images.in: Carneiro G Mateus D Peter L Deep learning and data labeling for medical applications. Springer International Publishing, New York City2016: 188-196
- Deep convolutional neural networks for image classification: a comprehensive review.Neural Comput. 2017; 29: 2352-2449
- Convolutional neural networks: an overview and application in radiology.Insights Imaging. 2018; 9: 611-629
- Comparisons between cervical vertebrae and hand-wrist maturation for assessment of skeletal maturity.J Clin Pediatr Dent. 1999; 24: 47-52
- Objective evaluation of cervical vertebral bone age’ its reliability in comparison with hand-wrist bone age: by TW3 method.J Contemp Dent Pract. 2013; 14: 806-813
- Cervical vertebral maturation method: reproducibility and efficiency of chronological age estimation.Appl Sci. 2021; 11: 3160
- Bone age assessment with various machine learning techniques: a systematic literature review and meta-analysis.PLoS One. 2019; 14e0220242
- Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models.Dentomaxillofac Radiol. 2020; 4920190441
- New method for evaluation of cervical vertebral maturation based on angular measurements.Saudi Med J. 2013; 34: 388-394
- Determination of growth and development periods in orthodontics with artificial neural network.Orthod Craniofac Res. 2021; 24: 76-83
- Estimating cervical vertebral maturation with a lateral cephalogram using the convolutional neural network.J Clin Med. 2021; 10: 5400
- Attention-guided multi-scale CNN network for cervical vertebral maturation assessment from lateral cephalometric radiography.Phys Sci Forum. 2022; 5: 26
- Deep learning and artificial intelligence for the determination of the cervical vertebra maturation degree from lateral radiography.Entropy. 2019; 21: 1222
Atici S, Pan H, Elnagar MH, et al. Classification of the Cervical Vertebrae Maturation (CVM) stages using the Tripod Network. arXiv. Preprint.
- Development of an artificial intelligence system for the automatic evaluation of cervical vertebral maturation status.Diagnostics (Basel). 2021; 11: 2200
- Deep learning for the classification of cervical maturation degree and pubertal growth spurts: a pilot study.Korean J Orthod. 2022; 52: 112-122
Article info
Publication history
Published online: March 06, 2023
Accepted:
February 15,
2023
Received in revised form:
February 15,
2023
Received:
November 20,
2022
Publication stage
In Press Corrected ProofFootnotes
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests: Authors have completed and submitted the ICMJE Form for Disclosure of potential conflicts of interest. None declared.
Provenance and peer review: Non-commissioned; externally peer reviewed.
Identification
Copyright
© 2023 World Federation of Orthodontists. Published by Elsevier Inc. All rights reserved.
