RT Journal Article
T1 XAS: Automatic yet eXplainable Age and Sex determination by combining imprecise per-tooth predictions
A1 Vila Blanco, Nicolás
A1 Varas Quintana, Paulina
A1 Aneiros Ardao, Ángela
A1 Tomás Carmona, Inmaculada
A1 Carreira Nouche, María José
K1 Deep learning
K1 Dental panoramic radiographs
K1 Tooth detection
K1 Chronological age prediction
K1 Sex classification
AB Chronological age and biological sex estimation are two key tasks in a variety of procedures, including human identification and migration control. Issues such as these have led to the development of both semiautomatic and automatic prediction models, but the former are expensive in terms of time and human resources, while the latter lack the interpretability required to be applicable in real-life scenarios. This paper therefore proposes a new, fully automatic methodology for the estimation of age and sex. This first applies a tooth detection by means of a modified CNN with the objective of extracting the oriented bounding boxes of each tooth. Then, it feeds the image features inside the tooth boxes into a second CNN module designed to produce per-tooth age and sex probability distributions. The method then adopts an uncertainty-aware policy to aggregate these estimated distributions. Our approach yielded a lower mean absolute error than any other previously described, at 0.97 years. The accuracy of the sex classification was 91.82%, confirming the suitability of the teeth for this purpose. The proposed model also allows analyses of age and sex estimations on every tooth, enabling experts to identify the most relevant for each task or population cohort or to detect potential developmental problems. In conclusion, the performance of the method in both age and sex predictions is excellent and has a high degree of interpretability, making it suitable for use in a wide range of application scenarios
PB Elsevier
YR 2022
FD 2022
LK http://hdl.handle.net/10347/29453
UL http://hdl.handle.net/10347/29453
LA eng
NO Computers in Biology and Medicine 149 (2022) 106072
DS Minerva
RD 28 abr 2026