  The brain-age gap is one of the most investigated risk markers for brain
changes across disorders. While the field is progressing towards large-scale
models, recently incorporating uncertainty estimates, no model to date provides
the single-subject risk assessment capability essential for clinical
application. In order to enable the clinical use of brain-age as a biomarker,
we here combine uncertainty-aware deep Neural Networks with conformal
prediction theory. This approach provides statistical guarantees with respect
to single-subject uncertainty estimates and allows for the calculation of an
individual's probability for accelerated brain-aging. Building on this, we show
empirically in a sample of N=16,794 participants that 1. a lower or comparable
error as state-of-the-art, large-scale brain-age models, 2. the statistical
guarantees regarding single-subject uncertainty estimation indeed hold for
every participant, and 3. that the higher individual probabilities of
accelerated brain-aging derived from our model are associated with Alzheimer's
Disease, Bipolar Disorder and Major Depressive Disorder.
