Age Estimation on Panoramic Dental X-ray Images using Deep Learning
Sarah Wallraﬀ , Sulaiman Vesal, Christopher Syben, Rainer Lutz, Andreas Maier
Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung
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.
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