Assessing demographic bias in brain age prediction models using multiple deep learning paradigms

Predicting brain age from structural Magnetic Resonance Imaging (MRI) has emerged as a critical task at the intersection of medical imaging and Artificial Intelligence, with Deep learning (DL) models achieving state-of-the-art performance. However, despite their predictive power, such models remain susceptible to algorithmic bias, especially when applied to populations whose demographic characteristics differ from those seen during training. In this paper, we investigate how demographic factors influence the performance of brain age prediction models. We leverage a large, demographically diverse MRI dataset including 7480 healthy subjects (3599 female and 3881 male) spanning three major racial groups: White, Black, and Asian. To explore the effects of data composition and model architecture on generalization, we design and compare multiple training paradigms, including models trained on single group and a Multi-Input architecture that explicitly incorporates demographic metadata. Results on an external test set including 3194 subjects (2162 White, 694 Black, and 338 Asian) reveal evidence of demographic bias, with the Multi-Input model achieving the most balanced performance across groups (mean absolute error: 2.94 ± 0.07 for White, 2.91 ± 0.16 for Black, and 3.34 ± 0.17 for Asian subjects). These findings highlight the need for fairness-aware approaches, advocating for strategies that mitigate bias, and enhance generalizability. • 3D CNNs for brain age prediction in structural MRI scans. • Demographic bias assessment across White, Black, and Asian subgroups. • Comparison of four training paradigms (TPs) in fairness and generalization.