UCLA

Identifying population differences can serve as an insightful tool for diagnostic radiology. To do so, a reliable preprocessing framework and data representation are vital. We consider building a machine learning model to visualize gender differences in the circle of Willis, (CoW) an integral part of the brain's vasculature. We start with a dataset of 570 individuals and process them for analysis using 389 for the final analysis. We find statistical differences between male and female patients for one viewpoint and can visualize where they are. In particular, we find that the interior cartoid atery (ICA) is larger in males than in females, and that the CoW is more likely to be complete in females. We also see differences between the right and left-hand sides of the brain confirmed using SVM. This process can be applied to automatically detecting population variations in the vasculature and can serve as a guide to explaining machine learning decisions.