  Age prediction is an important part of medical assessments and research. It
can aid in detecting diseases as well as abnormal ageing by highlighting the
discrepancy between chronological and biological age. To gain a comprehensive
understanding of age-related changes observed in various body parts, we
investigate them on a larger scale by using whole-body 3D images. We utilise
the Grad-CAM interpretability method to determine the body areas most
predictive of a person's age. We expand our analysis beyond individual subjects
by employing registration techniques to generate population-wide
interpretability maps. Our findings reveal three primary areas of interest: the
spine, the autochthonous back muscles, and the cardiac region, which exhibits
the highest importance.
