期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 149, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2022.106072
关键词
Deep learning; Dental panoramic radiographs; Tooth detection; Chronological age prediction; Sex classification
类别
资金
- Conselleria de Cultura, Educacion e Ordenacion Universitaria, Spain [ED431G-2019/04, ED431B 2020-2022 GPC2020/27]
- European Regional Development Fund (ERDF)
This paper proposes a new fully automatic methodology for the estimation of age and sex using tooth detection and convolutional neural networks. The method achieves high accuracy and interpretability in age and sex predictions.
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.
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