XAS: Automatic yet eXplainable Age and Sex determination by combining imprecise per-tooth predictions
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Título: | XAS: Automatic yet eXplainable Age and Sex determination by combining imprecise per-tooth predictions |
Autor/a: | Vila Blanco, Nicolás Varas Quintana, Paulina Aneiros Ardao, Ángela Tomás Carmona, Inmaculada Carreira Nouche, María José |
Centro/Departamento: | Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Información Universidade de Santiago de Compostela. Departamento de Cirurxía e Especialidades Médico-Cirúrxicas |
Palabras chave: | Deep learning | Dental panoramic radiographs | Tooth detection | Chronological age prediction | Sex classification | |
Data: | 2022 |
Editor: | Elsevier |
Cita bibliográfica: | Computers in Biology and Medicine 149 (2022) 106072 |
Resumo: | 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 |
Versión do editor: | https://doi.org/10.1016/j.compbiomed.2022.106072 |
URI: | http://hdl.handle.net/10347/29453 |
DOI: | 10.1016/j.compbiomed.2022.106072 |
E-ISSN: | 0010-4825 |
Dereitos: | © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Atribución 4.0 Internacional |
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