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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: herrjoon@uuh.ulsan.kr

† These authors contributed equally.

Abstract

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.


Keywords

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


Cite and Share

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.

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