  Deep learning has been shown to accurately assess 'hidden' phenotypes and
predict biomarkers from medical imaging beyond traditional clinician
interpretation of medical imaging. Given the black box nature of artificial
intelligence (AI) models, caution should be exercised in applying models to
healthcare as prediction tasks might be short-cut by differences in
demographics across disease and patient populations. 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. 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 - 0.71. When predicting race, we
show that tuning the proportion of a confounding variable (sex) in the training
data significantly impacts model AUC (ranging from 0.57 to 0.84), while in
training a sex prediction model, tuning a confounder (race) did not
substantially change AUC (0.81 - 0.83). This suggests a significant proportion
of the model's performance on predicting race could come from confounding
features being detected by AI. 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.
