# Chronological age estimation of lateral cephalometric radiographs with deep learning

arXiv: https://arxiv.org/abs/2101.11805 · HTML: https://mlsanity.com/paper/2101.11805/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 69 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 14 of 14)

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- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation](https://mlsanity.com/paper/2006.04868/) (2020)
- [Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19](https://mlsanity.com/paper/2004.02731/) (2020)
- [Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss](https://mlsanity.com/paper/1804.10916/) (2018)
- [Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection](https://mlsanity.com/paper/1906.04962/) (2019)
- [Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks](https://mlsanity.com/paper/1906.01795/) (2019)

## Abstract (arXiv)

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.