RT Dissertation/Thesis
T1 Deep learning for chronological age and sex prediction from dental panoramic radiographs
A1 Vila Blanco, Nicolás
K1 chronological age estimation
K1 sex prediction
K1 dental panoramic radiographs
K1 deep learning
K1 convolutional neural networks (CNNs)
AB Chronological age estimation and biological sex classification are tworelevant tasks in a variety of clinical procedures. Both of them are useful for the identification of human remains,legal age determination, validation of birth certificates, or control of migration flows, among others. Many bodyindicators have been used for both age and sex estimation. However, experts agree that the oral cavity hostsanatomical structures whose development correlates with chronological age to a large degree, and these structuresare different enough across males and females to enable accurate sex determination. Specifically, the teeth andmandible are considered good candidates due to their resistance and the ease of being radiologically observed. Inthe last decades, researchers and clinical experts developed numerous methods to convert dry-bone or radiologicmeasurements into an estimation of chronological age or sex. However, the application of these models has aseries of disadvantages, such as the inherent subjectivity caused by the expert's measurements or the timerequired to conduct the process. To alleviate these problems, this PhD Thesis introduces three different approachesbased on deep learning techniques for automatic age and sex estimation on dental panoramic radiographs. Thesemethods proved to be useful for assessing the suitability not only of the whole X-ray image but also of specificskeletal structures present in the image, such as the mandible and the teeth.
YR 2022
FD 2022
LK http://hdl.handle.net/10347/27713
UL http://hdl.handle.net/10347/27713
LA eng
DS Minerva
RD 23 abr 2026