  Age is one of the major known risk factors for Alzheimer's Disease (AD).
Detecting AD early is crucial for effective treatment and preventing
irreversible brain damage. Brain age, a measure derived from brain imaging
reflecting structural changes due to aging, may have the potential to identify
AD onset, assess disease risk, and plan targeted interventions. Deep
learning-based regression techniques to predict brain age from magnetic
resonance imaging (MRI) scans have shown great accuracy recently. However,
these methods are subject to an inherent regression to the mean effect, which
causes a systematic bias resulting in an overestimation of brain age in young
subjects and underestimation in old subjects. This weakens the reliability of
predicted brain age as a valid biomarker for downstream clinical applications.
Here, we reformulate the brain age prediction task from regression to
classification to address the issue of systematic bias. Recognizing the
importance of preserving ordinal information from ages to understand aging
trajectory and monitor aging longitudinally, we propose a novel ORdinal
Distance Encoded Regularization (ORDER) loss that incorporates the order of age
labels, enhancing the model's ability to capture age-related patterns.
Extensive experiments and ablation studies demonstrate that this framework
reduces systematic bias, outperforms state-of-art methods by statistically
significant margins, and can better capture subtle differences between clinical
groups in an independent AD dataset. Our implementation is publicly available
at https://github.com/jaygshah/Robust-Brain-Age-Prediction.
