AD-Diff: enhancing Alzheimer's disease prediction accuracy through multimodal fusion - Han, Lei (Frontiers in Computational Neuroscience, 2025)
Early prediction of Alzheimer's disease (AD) is crucial to improving patient quality of life and treatment outcomes. However, current predictive methods face challenges such as insufficient multimodal information integration and the high cost of PET image acquisition, which limit their effectiveness in practical applications. To address these issues, this paper proposes an innovative model, AD-Diff. This model significantly improves AD prediction accuracy by integrating PET images generated through a diffusion process with cognitive scale data and other modalities. Specifically, the AD-Diff model consists of two core components: the ADdiffusion module and the multimodal Mamba Classifier. The ADdiffusion module uses a 3D diffusion process to generate high-quality PET images, which are then fused with MRI images and tabular data to provide input for the Multimodal Mamba Classifier. Experimental results on the OASIS and ADNI datasets demonstrate that the AD-Diff model performs exceptionally well in both long-term and short-term AD prediction tasks, significantly improving prediction accuracy and reliability. These results highlight the significant advantages of the AD-Diff model in handling complex medical image data and multimodal information, providing an effective tool for the early diagnosis and personalized treatment of Alzheimer's disease.