IEEE Trans Med Imaging. 2020 Jul;39(7):2374-2384. doi: 10.1109/TMI.2020.2968765. Epub 2020 Jan 31.
Chronological age estimation is crucial labour in many clinical procedures, where the teeth have proven to be one of the best estimators. Although some methods to estimate the age from tooth measurements in orthopantomogram (OPG) images have been developed, they rely on time-consuming manual processes whose results are affected by the observer subjectivity. Furthermore, all those approaches have been tested only on OPG image sets of good radiological quality without any conditioning dental characteristic. In this work, two fully automatic methods to estimate the chronological age of a subject from the OPG image are proposed. The first (DANet) consists of a sequential Convolutional Neural Network (CNN) path to predict the age, while the second (DASNet) adds a second CNN path to predict the sex and uses sex-specific features with the aim of improving the age prediction performance. Both methods were tested on a set of 2289 OPG images of subjects from 4.5 to 89.2 years old, where both bad radiological quality images and images showing conditioning dental characteristics were not discarded. The results showed that the DASNet outperforms the DANet in every aspect, reducing the median Error (E) and the median Absolute Error (AE) by about 4 months in the entire database. When evaluating the DASNet in the reduced datasets, the AE values decrease as the real age of the subjects decreases, until reaching a median of about 8 months in the subjects younger than 15. The DASNet method was also compared to the state-of-the-art manual age estimation methods, showing significantly less over- or under-estimation problems. Consequently, we conclude that the DASNet can be used to automatically predict the chronological age of a subject accurately, especially in young subjects with developing dentitions.
年龄估计在许多临床程序中至关重要，而牙齿已被证明是最佳估计器之一。尽管已经开发出一些从全景牙科 X 光片（OPG）图像中的牙齿测量值来估计年龄的方法，但它们依赖于耗时的手动过程，其结果受观察者主观性的影响。此外，所有这些方法仅在没有任何条件性牙齿特征的良好放射学质量的 OPG 图像集上进行了测试。在这项工作中，提出了两种从 OPG 图像自动估计受试者年龄的方法。第一种（DANet）由一个顺序卷积神经网络（CNN）路径组成，用于预测年龄，而第二种（DASNet）添加了第二个 CNN 路径来预测性别，并使用性别特异性特征，旨在提高年龄预测性能。这两种方法都在一组年龄为 4.5 至 89.2 岁的 2289 名受试者的 OPG 图像上进行了测试，其中不仅包括放射学质量差的图像，还包括显示条件性牙齿特征的图像。结果表明，DASNet 在各个方面都优于 DANet，在整个数据库中，中位数误差（E）和中位数绝对误差（AE）分别减少了约 4 个月。在评估减少数据集的 DASNet 时，随着受试者真实年龄的降低，AE 值降低，直到在 15 岁以下的受试者中达到约 8 个月的中位数。DASNet 方法还与最新的手动年龄估计方法进行了比较，显示出明显更少的过度或低估问题。因此，我们得出结论，DASNet 可用于准确地自动预测受试者的年龄，特别是在具有发育牙列的年轻受试者中。
