Bone Age Assessment Techniques in Pediatric Dentistry

Bone age assessment is pivotal in appraising biological maturity and guiding orthodontic and restorative interventions in children. Traditional methods employ hand–wrist radiographs interpreted against standardised atlases or scoring systems to estimate skeletal maturity. In paediatric dentistry, orthopantomograms and cephalometric images of cervical vertebrae have become integral, offering concurrent evaluation of dental development and skeletal growth. Atlas-based approaches, such as the Greulich and Pyle atlas for hand bones and Demirjian’s dental staging, rely on visual comparison and practitioner expertise, resulting in inter-observer variability. Advances in image processing have facilitated semi-automatic and fully automatic pipelines that segment regions of interest, standardise radiographs and apply machine-learning algorithms for age prediction. Deep learning models, particularly convolutional neural networks, extract discriminative bone or dental features to achieve rapid and objective assessments. Research also highlights the need for population-specific standards owing to ethnic and sex differences in maturation. Collectively, these innovations promise enhanced accuracy, efficiency and consistency in bone age estimation within dental practice and allied clinical disciplines.

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Bone Age Assessment Techniques in Pediatric Dentistry publication trend

The graph below shows the total number of articles in bone age assessment techniques in pediatric dentistry across all publications each year (not limited to Nature Index journals).

Technical terms

Bone age: Measurement of skeletal maturity based on the developmental stage of bones, often compared against chronological age.

Orthopantomogram (OPG): A panoramic radiograph capturing the upper and lower jaws to assess dental and mandibular development.

Convolutional neural network (CNN): A class of deep learning models specialised for image analysis through hierarchical feature extraction.

Greulich and Pyle atlas: A standardised collection of hand–wrist radiographs used in manual assessment to estimate bone age.

Cervical vertebrae stages (CVS): A method for determining skeletal maturity by evaluating morphological changes in the second to fourth cervical vertebrae on lateral cephalometric images.

References

1. Bone age assessment based on deep neural networks with annotation-free cascaded critical bone region extraction. Frontiers in Artificial Intelligence (2023).
2. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Progress in Orthodontics (2019).
3. Deep Neural Networks for Chronological Age Estimation From OPG Images. IEEE Transactions on Medical Imaging (2020).
4. Is the Greulich and Pyle atlas applicable to all ethnicities? A systematic review and meta-analysis. European Radiology (2019).
5. Ethnic and sex differences in skeletal maturation among the Birth to Twenty cohort in South Africa. Archives of Disease in Childhood (2014).
6. Assessment of Dental Age of Children Aged 3.5 to 16.9 Years Using Demirjian’s Method: A Meta-Analysis Based on 26 Studies. PLOS ONE (2013).
7. Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method. European Radiology Experimental (2020).