Confounders mediate AI prediction of demographics in medical imaging

• Published in: NPJ digital medicine Vol. 5; no. 1; p. 188
• Main Authors: Duffy, Grant, Clarke, Shoa L., Christensen, Matthew, He, Bryan, Yuan, Neal, Cheng, Susan, Ouyang, David
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.12.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/2397; Age; Artificial intelligence; Biomedicine; Biotechnology; Confounding (Statistics); Deep learning; Demographics; Medical imaging; Medicine; Medicine & Public Health
• DOI: 10.1038/s41746-022-00720-8
• ISSN: 2398-6352

Deep learning has been shown to accurately assess “hidden” phenotypes from medical imaging beyond traditional clinician interpretation. Using large echocardiography datasets from two healthcare systems, we test whether it is possible to predict age, race, and sex from cardiac ultrasound images using deep learning algorithms and assess the impact of varying confounding variables. Using a total of 433,469 videos from Cedars-Sinai Medical Center and 99,909 videos from Stanford Medical Center, we trained video-based convolutional neural networks to predict age, sex, and race. We found that deep learning models were able to identify age and sex, while unable to reliably predict race. Without considering confounding differences between categories, the AI model predicted sex with an AUC of 0.85 (95% CI 0.84–0.86), age with a mean absolute error of 9.12 years (95% CI 9.00–9.25), and race with AUCs ranging from 0.63 to 0.71. When predicting race, we show that tuning the proportion of confounding variables (age or sex) in the training data significantly impacts model AUC (ranging from 0.53 to 0.85), while sex and age prediction was not particularly impacted by adjusting race proportion in the training dataset AUC of 0.81–0.83 and 0.80–0.84, respectively. This suggests significant proportion of AI’s performance on predicting race could come from confounding features being detected. Further work remains to identify the particular imaging features that associate with demographic information and to better understand the risks of demographic identification in medical AI as it pertains to potentially perpetuating bias and disparities.