  Age is an essential factor in modern diagnostic procedures. However,
assessment of the true biological age (BA) remains a daunting task due to the
lack of reference ground-truth labels. Current BA estimation approaches are
either restricted to skeletal images or rely on non-imaging modalities that
yield a whole-body BA assessment. However, various organ systems may exhibit
different aging characteristics due to lifestyle and genetic factors. In this
initial study, we propose a new framework for organ-specific BA estimation
utilizing 3D magnetic resonance image (MRI) scans. As a first step, this
framework predicts the chronological age (CA) together with the corresponding
patient-dependent aleatoric uncertainty. An iterative training algorithm is
then utilized to segregate atypical aging patients from the given population
based on the predicted uncertainty scores. In this manner, we hypothesize that
training a new model on the remaining population should approximate the true BA
behavior. We apply the proposed methodology on a brain MRI dataset containing
healthy individuals as well as Alzheimer's patients. We demonstrate the
correlation between the predicted BAs and the expected cognitive deterioration
in Alzheimer's patients.
