XAS: Automatic yet eXplainable Age and Sex determination by combining imprecise per-tooth predictions. (October 2022)

• Record Type: Journal Article
• Authors: Vila-Blanco, Nicolás; Varas-Quintana, Paulina; Aneiros-Ardao, Ángela; Tomás, Inmaculada; Carreira, María J.
• Is Part Of: Computers in biology and medicine. Volume 149(2022)
• Publication Date: 2022-10
• Subjects: Deep learning -- Dental panoramic radiographs -- Tooth detection -- Chronological age prediction -- Sex classification; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106072
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: A novel two-step deep learning architecture for estimating age and sex automatically from dental radiographs. An efficient CNN module proposal to exploit common and independent features across the different tooth types. An uncertainty-aware approach to combine the predicted per-tooth probability distributions of age and sex. High degree of explainability with graphical representations of the per-tooth predictions and the uncertainty. State-of-the-art performance, specially in age estimation.