# Age prediction using a large chest X-ray dataset

arXiv: https://arxiv.org/abs/1903.06542 · HTML: https://mlsanity.com/paper/1903.06542/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [eXplainable Artificial Intelligence (XAI) in aging clock models](https://mlsanity.com/paper/2307.13704/) (2023)
- [Assessing the Performance of Automated Prediction and Ranking of Patient Age from Chest X-rays Against Clinicians](https://mlsanity.com/paper/2207.01302/) (2022)
- [Visualizing chest X-ray dataset biases using GANs](https://mlsanity.com/paper/2305.00147/) (2023)
- [X-ray Recognition: Patient identification from X-rays using a contrastive objective](https://mlsanity.com/paper/2305.00149/) (2023)

## Abstract (arXiv)

Age prediction based on appearances of different anatomies in medical images has been clinically explored for many decades. In this paper, we used deep learning to predict a persons age on Chest X-Rays. Specifically, we trained a CNN in regression fashion on a large publicly available dataset. Moreover, for interpretability, we explored activation maps to identify which areas of a CXR image are important for the machine (i.e. CNN) to predict a patients age, offering insight. Overall, amongst correctly predicted CXRs, we see areas near the clavicles, shoulders, spine, and mediastinum being most activated for age prediction, as one would expect biologically. Amongst incorrectly predicted CXRs, we have qualitatively identified disease patterns that could possibly make the anatomies appear older or younger than expected. A further technical and clinical evaluation would improve this work. As CXR is the most commonly requested imaging exam, a potential use case for estimating age may be found in the preventative counseling of patient health status compared to their age-expected average, particularly when there is a large discrepancy between predicted age and the real patient age.