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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Age Estimation from Lateral Cephalograms Using Deep Learning: A Pilot Study from Early Childhood to Older Adults

by
Ryohei Tokinaga
1,†,
Yuichi Mine
1,2,*,†,
Yuki Yoshimi
3,
Shota Okazaki
1,2,4,
Shota Ito
3,
Saori Takeda
1,2,
Saki Ogawa
3,
Tzu-Yu Peng
5,
Naoya Kakimoto
6,
Kotaro Tanimoto
3 and
Takeshi Murayama
1,2
1
Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima 734-8553, Japan
2
Project Research Center for Integrating Digital Dentistry, Hiroshima University, Hiroshima 734-8553, Japan
3
Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima 734-8553, Japan
4
AIT Center, Sapporo City University, Sapporo 060-0061, Japan
5
School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei 11031, Taiwan
6
Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima 734-8553, Japan
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Clin. Med. 2025, 14(19), 7084; https://doi.org/10.3390/jcm14197084
Submission received: 10 September 2025 / Revised: 29 September 2025 / Accepted: 6 October 2025 / Published: 7 October 2025
(This article belongs to the Special Issue Artificial Intelligence (AI) in Dental Clinical Practice)
Versions Notes

Abstract

Background/Objectives: The purpose of this study is twofold: first, to construct and evaluate a deep-learning model for automated age estimation from lateral cephalograms spanning early childhood to older adulthood; and second, to determine whether sex-specific training improves predictive accuracy. Methods: This retrospective study examined 600 lateral cephalograms (ages 4–63 years; 300 female, 300 male). The images were randomly divided into five cross-validation folds, stratified by sex and age. An ImageNet-pretrained DenseNet-121 was employed for age regression. Three networks were trained: mixed-sex, female-only, and male-only. Performance was evaluated using mean absolute error (MAE) and the coefficient of determination (R2). Grad-CAM heatmaps quantified the contributions of six craniofacial regions. Duplicate patients were excluded to minimize sampling bias. Results: The mixed-sex model achieved an MAE of 2.50 ± 0.27 years, an R2 of 0.84 ± 0.04, the female-only model achieved an MAE of 3.04 ± 0.37 years and an R2 of 0.82 ± 0.04, and the male-only model achieved an MAE of 2.29 ± 0.27 years and an R2 of 0.83 ± 0.04. Grad-CAM revealed dominant activations over the frontal bone in the mixed-sex model; the occipital bone and cervical soft tissue in the female model; and the parietal bone in the male model. Conclusions: A DenseNet-121-based analysis of lateral cephalograms can provide a clinically relevant age estimation with an error margin of approximately ±2.5 years. Using male-only model slightly improves performance metrics, and careful attention to training data distribution is crucial for broad applicability. Our findings suggest a potential contribution to forensic age estimation, growth and development research, and support for unidentified deceased individuals when dental records are unavailable.
Keywords: artificial intelligence; deep learning; lateral cephalograms; age estimation artificial intelligence; deep learning; lateral cephalograms; age estimation

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MDPI and ACS Style

Tokinaga, R.; Mine, Y.; Yoshimi, Y.; Okazaki, S.; Ito, S.; Takeda, S.; Ogawa, S.; Peng, T.-Y.; Kakimoto, N.; Tanimoto, K.; et al. Age Estimation from Lateral Cephalograms Using Deep Learning: A Pilot Study from Early Childhood to Older Adults. J. Clin. Med. 2025, 14, 7084. https://doi.org/10.3390/jcm14197084

AMA Style

Tokinaga R, Mine Y, Yoshimi Y, Okazaki S, Ito S, Takeda S, Ogawa S, Peng T-Y, Kakimoto N, Tanimoto K, et al. Age Estimation from Lateral Cephalograms Using Deep Learning: A Pilot Study from Early Childhood to Older Adults. Journal of Clinical Medicine. 2025; 14(19):7084. https://doi.org/10.3390/jcm14197084

Chicago/Turabian Style

Tokinaga, Ryohei, Yuichi Mine, Yuki Yoshimi, Shota Okazaki, Shota Ito, Saori Takeda, Saki Ogawa, Tzu-Yu Peng, Naoya Kakimoto, Kotaro Tanimoto, and et al. 2025. "Age Estimation from Lateral Cephalograms Using Deep Learning: A Pilot Study from Early Childhood to Older Adults" Journal of Clinical Medicine 14, no. 19: 7084. https://doi.org/10.3390/jcm14197084

APA Style

Tokinaga, R., Mine, Y., Yoshimi, Y., Okazaki, S., Ito, S., Takeda, S., Ogawa, S., Peng, T.-Y., Kakimoto, N., Tanimoto, K., & Murayama, T. (2025). Age Estimation from Lateral Cephalograms Using Deep Learning: A Pilot Study from Early Childhood to Older Adults. Journal of Clinical Medicine, 14(19), 7084. https://doi.org/10.3390/jcm14197084

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