Analysing race and sex bias in brain age prediction
Carolina Piçarra Ben Glocker
Brain age prediction from MRI has become a popular imaging biomarker
associated with a wide range of neuropathologies. The datasets used for
training, however, are often skewed and imbalanced regarding demographics,
potentially making brain age prediction models susceptible to bias. We analyse
the commonly used ResNet-34 model by conducting a comprehensive subgroup
performance analysis and feature inspection. The model is trained on 1,215
T1-weighted MRI scans from Cam-CAN and IXI, and tested on UK Biobank
(n=42,786), split into six racial and biological sex subgroups. With the
objective of comparing the performance between subgroups, measured by the
absolute prediction error, we use a Kruskal-Wallis test followed by two
post-hoc Conover-Iman tests to inspect bias across race and biological sex. To
examine biases in the generated features, we use PCA for dimensionality
reduction and employ two-sample Kolmogorov-Smirnov tests to identify
distribution shifts among subgroups. Our results reveal statistically
significant differences in predictive performance between Black and White,
Black and Asian, and male and female subjects. Seven out of twelve pairwise
comparisons show statistically significant differences in the feature
distributions. Our findings call for further analysis of brain age prediction
models.