Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Spain; Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Spain.
Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical Surgical Specialities, School of Medicine and Dentistry, Universidade de Santiago de Compostela, Spain.
Comput Biol Med. 2022 Oct;149:106072. doi: 10.1016/j.compbiomed.2022.106072. Epub 2022 Sep 5.
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.
年龄和生物性别估计是各种程序中的两个关键任务，包括人类识别和移民控制。这些问题导致了半自动和自动预测模型的发展，但前者在时间和人力资源方面成本高昂，而后者缺乏在实际场景中应用所需的可解释性。因此，本文提出了一种新的、完全自动的年龄和性别估计方法。该方法首先通过修改后的 CNN 进行牙齿检测，目的是提取每个牙齿的定向边界框。然后，它将牙齿框内的图像特征输入到第二个 CNN 模块中，该模块旨在生成每颗牙齿的年龄和性别概率分布。然后，该方法采用不确定感知策略来聚合这些估计的分布。与之前描述的任何方法相比，我们的方法产生的平均绝对误差更低，为 0.97 岁。性别分类的准确率为 91.82%，证实了牙齿在这方面的适用性。该模型还允许对每颗牙齿的年龄和性别估计进行分析，使专家能够识别出对每个任务或人群队列最相关的牙齿，或检测潜在的发育问题。总之，该方法在年龄和性别预测方面的性能非常出色，具有高度的可解释性，适用于广泛的应用场景。
