  The traditional manual age estimation method is crucial labor based on many
kinds of the X-Ray image. Some current studies have shown that lateral
cephalometric(LC) images can be used to estimate age. However, these methods
are based on manually measuring some image features and making age estimates
based on experience or scoring. Therefore, these methods are time-consuming and
labor-intensive, and the effect will be affected by subjective opinions. In
this work, we propose a saliency map-enhanced age estimation method, which can
automatically perform age estimation based on LC images. Meanwhile, it can also
show the importance of each region in the image for age estimation, which
undoubtedly increases the method's Interpretability. Our method was tested on
3014 LC images from 4 to 40 years old. The MEA of the experimental result is
1.250, which is less than the result of the state-of-the-art benchmark because
it performs significantly better in the age group with fewer data. Besides, our
model is trained in each area with a high contribution to age estimation in LC
images, so the effect of these different areas on the age estimation task was
verified. Consequently, we conclude that the proposed saliency map enhancements
chronological age estimation method of lateral cephalometric radiographs can
work well in chronological age estimation task, especially when the amount of
data is small. Besides, compared with traditional deep learning, our method is
also interpretable.
