Abstract
Dental panoramic X-ray images provide important information about an adolescent's age because the sequential development process of teeth is one of the longest in the human body. Such dental panoramic projections can be used to assess the age of a person. However, the existing manual methods for age estimation suffer from a low accuracy rate. In this study, we propose a supervised regression-based deep learning method for automatic age estimation of adolescents aged 11 to 20 years to reduce this estimation error. To evaluate the model performance, we used a new dental panoramic X-ray data set with 14,000 images of patients in the considered age range. In an early investigation, our proposed method achieved a mean absolute error (MAE) of 1.08 years and error-rate (ER) of 17.52% on the test data set, which clearly outperformed the dental experts' estimation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
AlQahtani SJ, Hector MP, Liversidge HM. Accuracy of dental age estimation charts: Schour and massler, ubelaker, and the london atlas. Am J Phys Anthropol. 2014;154(1):70–78.
Müller N. Zur Altersbestimmung beim Menschen unter besonderer Berücksichtigung der Weisheitszähne. Universität Erlangen-Nürnberg: Dissertation; 1990.
AlQahtani SJ, Hector MP, Liversidge HM. The london atlas of human tooth development and eruption. Am J Phys Anthropol. 2010;142(3):481–490.
Demirjian A, Goldstein H, Tanner JM. A new system of dental age assessment. Hum Biol. 1973;45(2):211–227.
Khorate MM, Dinkar AD, Ahmed J. Accuracy of age estimation methods from orthopantomograph in forensic odontology: A comparative study. Forensic Sci Int. 2013;184.
Stern D, Kainz P, et al. Multi-factorial age estimation from skeletal and dental MRI volumes. In: Machine Learning in Med Im. Cham: Springer International; 2017. p. 61–69.
Kahaki SMM, Nordin J, et al. Deep convolutional neural network designed for age assessment based on orthopantomography data. In: Neural Computing and Applications. vol. 32. Springer; 2019. p. 9357–9368.
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2015; p. 770–778.
Chattopadhyay A, Sarkar A, et al. Grad-CAM++: Improved visual explanations for deep convolutional networks. Proc IEEE Workshop Appl Comput Vis. 2018; p. 839–847.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Wallraff, S., Vesal, S., Syben, C., Lutz, R., Maier, A. (2021). Age Estimation on Panoramic Dental X-ray Images using Deep Learning. In: Palm, C., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_46
Download citation
DOI: https://doi.org/10.1007/978-3-658-33198-6_46
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-33197-9
Online ISBN: 978-3-658-33198-6
eBook Packages: Computer Science and Engineering (German Language)