Ability of artificial intelligence to identify self-reported race in chest x-ray using pixel intensity counts

John Lee Burns; Zachary Zaiman; Jack Vanschaik; Gaoxiang Luo; Le Peng; Brandon Price; Garric Mathias; Vijay Mittal; Akshay Sagane; Christopher Tignanelli; Sunandan Chakraborty; Judy W. Gichoya; Saptarshi Purkayastha

doi:10.1117/1.JMI.10.6.061106

4 August 2023 Ability of artificial intelligence to identify self-reported race in chest x-ray using pixel intensity counts

John Lee Burns, Zachary Zaiman, Jack Vanschaik, Gaoxiang Luo, Le Peng, Brandon Price, Garric Mathias, Vijay Mittal, Akshay Sagane, Christopher Tignanelli, Sunandan Chakraborty, Judy W. Gichoya, Saptarshi Purkayastha

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 6, 061106 (August 2023). https://doi.org/10.1117/1.JMI.10.6.061106

Abstract

Purpose

Prior studies show convolutional neural networks predicting self-reported race using x-rays of chest, hand and spine, chest computed tomography, and mammogram. We seek an understanding of the mechanism that reveals race within x-ray images, investigating the possibility that race is not predicted using the physical structure in x-ray images but is embedded in the grayscale pixel intensities.

Approach

Retrospective full year 2021, 298,827 AP/PA chest x-ray images from 3 academic health centers across the United States and MIMIC-CXR, labeled by self-reported race, were used in this study. The image structure is removed by summing the number of each grayscale value and scaling to percent per image (PPI). The resulting data are tested using multivariate analysis of variance (MANOVA) with Bonferroni multiple-comparison adjustment and class-balanced MANOVA. Machine learning (ML) feed-forward networks (FFN) and decision trees were built to predict race (binary Black or White and binary Black or other) using only grayscale value counts. Stratified analysis by body mass index, age, sex, gender, patient type, make/model of scanner, exposure, and kilovoltage peak setting was run to study the impact of these factors on race prediction following the same methodology.

Results

MANOVA rejects the null hypothesis that classes are the same with 95% confidence (F 7.38, P < 0.0001) and balanced MANOVA (F 2.02, P < 0.0001). The best FFN performance is limited [area under the receiver operating characteristic (AUROC) of 69.18%]. Gradient boosted trees predict self-reported race using grayscale PPI (AUROC 77.24%).

Conclusions

Within chest x-rays, pixel intensity value counts alone are statistically significant indicators and enough for ML classification tasks of patient self-reported race.

Citation Download Citation

John Lee Burns, Zachary Zaiman, Jack Vanschaik, Gaoxiang Luo, Le Peng, Brandon Price, Garric Mathias, Vijay Mittal, Akshay Sagane, Christopher Tignanelli, Sunandan Chakraborty, Judy W. Gichoya, and Saptarshi Purkayastha "Ability of artificial intelligence to identify self-reported race in chest x-ray using pixel intensity counts," Journal of Medical Imaging 10(6), 061106 (4 August 2023). https://doi.org/10.1117/1.JMI.10.6.061106

Received: 30 January 2023; Accepted: 18 July 2023; Published: 4 August 2023

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KEYWORDS

Artificial intelligence

Data modeling

Chest imaging

Binary data

Brain-machine interfaces

X-rays

Machine learning

Purpose

Approach

Results

Conclusions

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