Age prediction using a large chest X-ray dataset

TL;DR

This study employs deep learning on a large chest X-ray dataset to predict patient age, providing interpretability insights and potential clinical applications in health assessment and disease detection.

Contribution

It introduces a CNN-based regression model for age prediction from CXRs and explores activation maps for interpretability, linking image regions to age estimation.

Findings

Activation near clavicles, shoulders, spine, mediastinum for age prediction

Incorrect predictions linked to disease patterns affecting apparent age

Potential for clinical use in health status assessment

Abstract

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.