Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation

Mild cognitive impairment (MCI) is widely recognized as a highly heterogeneous and critical prodromal stage of dementia. Emerging evidence reveals that MCI is a systemic condition, with pathological and metabolic alterations manifesting in both the brain and peripheral organs. However, current computer-aided diagnostic methods primarily use brain-only images and often overlook pathological alterations in peripheral organs associated with MCI progression. To address this limitation, we propose MOGAD-Net, a multi-organ guided alignment and distillation network, which uses the fused systemic pathological features from the brain, heart, and gut to guide MCI detection in the training stage, while using only brain images in the application stage. Specifically, a panoramic Swin Transformer (PanSwin) encoder is introduced across all phases of MOGAD-Net to effectively capture global features across diverse organ images. Building on this architecture, MOGAD-Net first uses a semi-supervised multi-organ collaborative framework to distinguish patients with MCI from individuals with normal cognition (NC). This framework aligns heart and gut feature representations with brain feature representations via a hierarchical feature alignment module, facilitating inter-organ feature fusion. Furthermore, to improve clinical applicability, MOGAD-Net uses a hierarchical-constraint knowledge distillation module to transfer diagnostic knowledge from the multi-organ network to a brain-only model. Experiments on seven datasets show that MOGAD-Net significantly outperforms state-of-the-art methods in distinguishing MCI from NC.