Age prediction using a large chest x-ray dataset

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 person's 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 patient's 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. 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 counselling 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.

Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning

“…Similar deep learning methods were used on two large sets of frontal CXRs and demonstrated predictive power for mortality [29]. In another study [30], deep learning was used on a large public data set to train a model to predict age from a frontal CXR. These studies all suggest that the CXR can serve as a complex biomarker.…”

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

“…Another approach to general classification was age prediction. Karargyris et al [175] aimed to predict the patient's age from his CXR and compare it to his actual age to improve counseling particularly when there is a notable difference. CNN was trained in regression on a large publicly available dataset, and heat maps were explored to realize the significance of areas near spine, shoulders, mediastinum and clavicles for age prediction.…”

“…New applications to medical deep learning on chest were introduced the past 4 years, such as to age prediction learning model by Karargyris et al [175] that aims to predict the age of a patient from his medical scan, where a gap between the predicted age and the real one indicates a health concern. Another approach by Wong et al [176] classifies the normal scans to disregard them prioritizing diagnosis of abnormal ones.…”

Using deep learning to analyze the compositeness of musculoskeletal aging reveals that spine, hip and knee age at different rates, and are associated with different genetic and non-genetic factors

“…Biological age predictors can be built by training machine learning algorithms to predict chronological age from biomedical features, with the biological age of a participant being defined as the prediction outputted by the model. Others have used full body 10 , chest 11 , hip [12][13][14][15] , knee [16][17][18][19][20][21][22] or hand [23][24][25][26][27][28][29][30] X-ray images to predict age.…”