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Original Research

Open Access

Comparative analysis of deep-learning-based bone age estimation between whole lateral cephalometric and the cervical vertebral region in children

  • Suhae Kim1,†
  • Jonghyun Shin1,2,†
  • Eungyung Lee1,2
  • Soyoung Park1,2
  • Taesung Jeong1,2
  • JaeJoon Hwang2,3
  • Hyejun Seo4,*,

1Department of Pediatric Dentistry, School of Dentistry, Pusan National University, 50612 Yangsan, Republic of Korea

2Dental and Life Science Institute & Dental Research Institute, School of Dentistry, Pusan National University, 50612 Yangsan, Republic of Korea

3Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, 50612 Yangsan, Republic of Korea

4Department of Dentistry, Ulsan University Hospital, 44033 Ulsan, Republic of Korea

DOI: 10.22514/jocpd.2024.093 Vol.48,Issue 4,July 2024 pp.191-199

Submitted: 31 October 2023 Accepted: 19 December 2023

Published: 03 July 2024

*Corresponding Author(s): Hyejun Seo E-mail:

† These authors contributed equally.


Bone age determination in individuals is important for the diagnosis and treatment of growing children. This study aimed to develop a deep-learning model for bone age estimation using lateral cephalometric radiographs (LCRs) and regions of interest (ROIs) in growing children and evaluate its performance. This retrospective study included 1050 patients aged 4–18 years who underwent LCR and hand-wrist radiography on the same day at Pusan National University Dental Hospital and Ulsan University Hospital between January 2014 and June 2023. Two pretrained convolutional neural networks, InceptionResNet-v2 and NasNet-Large, were employed to develop a deep-learning model for bone age estimation. The LCRs and ROIs, which were designated as the cervical vertebrae areas, were labeled according to the patient’s bone age. Bone age was collected from the same patient’s hand-wrist radiograph. Deep-learning models trained with five-fold cross-validation were tested using internal and external validations. The LCR-trained model outperformed the ROI-trained models. In addition, visualization of each deep learning model using the gradient-weighted regression activation mapping technique revealed a difference in focus in bone age estimation. The findings of this comparative study are significant because they demonstrate the feasibility of bone age estimation via deep learning with craniofacial bones and dentition, in addition to the cervical vertebrae on the LCR of growing children.


Bone age; Convolutional neural network; Deep learning; Lateral cephalometric radiograph; Skeletal maturity

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Suhae Kim,Jonghyun Shin,Eungyung Lee,Soyoung Park,Taesung Jeong,JaeJoon Hwang,Hyejun Seo. Comparative analysis of deep-learning-based bone age estimation between whole lateral cephalometric and the cervical vertebral region in children. Journal of Clinical Pediatric Dentistry. 2024. 48(4);191-199.


[1] Hunter CJ. The correlation of facial growth with body height and skeletal maturation at adolescence. The Angle Orthodontist. 1966; 36: 44–54.

[2] Hägg U, Taranger J. Maturation indicators and the pubertal growth spurt. American Journal of Orthodontics. 1982; 82: 299–309.

[3] Lamparski DG. Skeletal age assessment utilizing cervical vertebrae. American Journal of Orthodontics. 1975; 67: 458–459.

[4] Hägg U, Taranger J. Skeletal stages of the hand and wrist as indicators of the pubertal growth spurt. Acta Odontologica Scandinavica. 1980; 38: 187–200.

[5] Flores-Mir C, Nebbe B, Major PW. Use of skeletal maturation based on hand-wrist radiographic analysis as a predictor of facial growth: a systematic review. The Angle Orthodontist. 2004;74:118–124.

[6] Mito T, Sato K, Mitani H. Cervical vertebral bone age in girls. American Journal of Orthodontics and Dentofacial Orthopedics. 2002; 122: 380–385.

[7] Utama V, Soedarsono N, Yuniastuti M. Assessment of agreement between cervical vertebrae skeletal and dental age estimation with chronological age in an Indonesian population. The Journal of Forensic Odonto-stomatology. 2020; 38: 16.

[8] Lin L, Tang B, Cao L, Yan J, Zhao T, Hua F, et al. The knowledge, experience, and attitude on artificial intelligence-assisted cephalometric analysis: survey of orthodontists and orthodontic students. American Journal of Orthodontics and Dentofacial Orthopedics. 2023; 164: e97–e105.

[9] Law M, Seah J, Shih G. Artificial intelligence and medical imaging: applications, challenges and solutions. Medical Journal of Australia. 2021; 214: 450.

[10] Barragán-Montero A, Javaid U, Valdés G, Nguyen D, Desbordes P, Macq B, et al. Artificial intelligence and machine learning for medical imaging: a technology review. Physica Medica. 2021; 83: 242–256.

[11] Schwendicke F, Golla T, Dreher M, Krois J. Convolutional neural networks for dental image diagnostics: a scoping review. Journal of Dentistry. 2019; 91: 103226.

[12] Seo H, Hwang J, Jung Y, Lee E, Nam OH, Shin J. Deep focus approach for accurate bone age estimation from lateral cephalogram. Journal of Dental Sciences. 2023; 18: 34–43.

[13] Sun S, Zhang R. Region of interest extraction of medical image based on improved region growing algorithm. 2017 International Conference on Material Science, Energy and Environmental Engineering (MSEEE 2017). Atlantis Press. 2017; 471–475.

[14] Jangam D, Kale P, Fatema S. Age determination using lateral cephalogram and orthopantomograph: a comparative study. Scholars Journal of Applied Medical Sciences. 2014; 2: 987–990.

[15] Murali K, Nirmal RM, Balakrishnan S, Shanmugam S, Altaf SK, Nandhini D. Age estimation using cephalometrics—a cross-sectional study among teenagers of salem district, Tamil Nadu. Journal of Pharmacy and Bioallied Sciences. 2023; 15: S725–S728.

[16] Zhang Z, Liu N, Guo Z, Jiao L, Fenster A, Jin W, et al. Ageing and degeneration analysis using ageing-related dynamic attention on lateral cephalometric radiographs. npj Digital Medicine. 2022; 5: 151.

[17] Lee SY, Im SA. Comparison of bone agesin early puberty: computerized greulich-pyle based bone age vs. sauvegrain method. Journal of the Korean Society of Radiology. 2022; 83: 1081.

[18] Seo H, Hwang J, Jeong T, Shin J. Comparison of deep learning models for cervical vertebral maturation stage classification on lateral cephalometric radiographs. Journal of Clinical Medicine. 2021; 10: 3591.

[19] Yu H, Yang LT, Zhang Q, Armstrong D, Deen MJ. Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives. Neurocomputing. 2021; 444: 92–110.

[20] Wightman R, Touvron H, Jégou H. Resnet strikes back: an improved training procedure in timm. arXiv 2021. 2021; 1–22.

[21] Zoph B, Vasudevan V, Shlens J, Le QV. Learning transferable architectures for scalable image recognition. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018; 15: 8697–8710.

[22] Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. 2017 IEEE International Conference on Computer Vision (ICCV). 2017; 618–626.

[23] Kim EG, Oh IS, So JE, Kang J, Le VNT, Tak MK, et al. Estimating cervical vertebral maturation with a lateral cephalogram using the convolutional neural network. Journal of Clinical Medicine. 2021; 10: 5400.

[24] Kim JR, Shim WH, Yoon HM, Hong SH, Lee JS, Cho YA, et al. Computerized bone age estimation using deep learning based program: evaluation of the accuracy and efficiency. American Journal of Roentgenology. 2017; 209: 1374–1380.

[25] Ramspek CL, Jager KJ, Dekker FW, Zoccali C, van Diepen M. External validation of prognostic models: what, why, how, when and where? Clinical Kidney Journal. 2021; 14: 49–58.

[26] Giuste F, Shi W, Zhu Y, Naren T, Isgut M, Sha Y, et al. Explainable artificial intelligence methods in combating pandemics: a systematic review. IEEE Reviews in Biomedical Engineering. 2023; 16: 5–21.

[27] Dadgar S, Hadian H, Ghobadi M, Sobouti F, Rakhshan V. Correlations among chronological age, cervical vertebral maturation index, and Demirjian developmental stage of the maxillary and mandibular canines and second molars. Surgical and Radiologic Anatomy. 2021; 43: 131–143.

[28] Kim SJ, Song JS, Kim I, Kim S, Choi H. Correlation between dental and skeletal maturity in Korean children. The Journal of the Korean Academy of Pedtatric Dentistry. 2021; 48: 255–268.

[29] Jaworek-Troć J, Zarzecki M, Bonczar A, et al. Sphenoid bone and its sinus: anatomo-clinical review of the literature including application to FESS. Folia Medica Cracoviensia. 2019;59:

[30] Lai EH, Liu J, Chang JZ, Tsai S, Yao CJ, Chen M, et al. Radiographic assessment of skeletal maturation stages for orthodontic patients: hand-wrist bones or cervical vertebrae? Journal of the Formosan Medical Association. 2008; 107: 316–325.

[31] Baccetti T, Franchi L, McNamara JA. The cervical vertebral maturation (CVM) method for the assessment of optimal treatment timing in dentofacial orthopedics. Seminars in Orthodontics. 2005; 11: 119–129.

[32] McNamara JA, Franchi L. The cervical vertebral maturation method: a user’s guide. The Angle Orthodontist. 2018; 88: 133–143.

[33] Gabriel DB, Southard KA, Qian F, Marshall SD, Franciscus RG, Southard TE. Cervical vertebrae maturation method: poor reproducibility. American Journal of Orthodontics and Dentofacial Orthopedics. 2009; 136: 478.e1–478.e7.

[34] Satoh M, Tanaka T. Bone age at onset of pubertal growth spurt and final height in normal children. Clinical Pediatric Endocrinology. 1995; 4: 129–136.

[35] Satoh M. Bone age: assessment methods and clinical applications. Clinical Pediatric Endocrinology. 2015; 24: 143–152.

[36] Reinehr T, Carlsson M, Chrysis D, Camacho-Hübner C. Adult height prediction by bone age determination in children with isolated growth hormone deficiency. Endocrine Connections. 2020; 9: 370–378.

[37] Suh J, Heo J, Kim SJ, Park S, Jung MK, Choi HS, et al. Bone age estimation and prediction of final adult height using deep learning. Yonsei Medical Journal. 2023; 64: 679.

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