Predicting the stages of Alzheimer's disease (AD) is crucial for delaying disease progression and enabling early intervention. A large amount of existing research focuses on the classification of cognitively normal (CN), mild cognitive impairment (MCI), and AD. However, the two subtypes of MCI-stable mild cognitive impairment (sMCI) and progressive mild cognitive impairment (pMCI)-should not be overlooked. Therefore, this study aims to accurately diagnose the disease stage of patients (CN, MCI, or AD) and further distinguish between sMCI and pMCI. In this work, a multi-task classification model based on multi-source feature fusion, termed MTC-MSFFNet, is proposed to accomplish two diagnostic tasks: (1) CN vs. MCI vs. AD, and (2) sMCI vs. pMCI.We select the hippocampus (HIP) and entorhinal cortex (ERC) as feature maps for the three-class task, and the hippocampus (HIP) and gray matter (GM) for the sMCI/pMCI task. The MTC-MSFFNet integrates a multi-source feature fusion module which combining brain structure maps with structural magnetic resonance imaging (sMRI) data, a task-specific weight learning module guided by brain structural information, and dedicated task heads for each classification objective. The proposed method is evaluated on a mixed dataset constructed from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS). Experimental results demonstrate that MTC-MSFFNet achieves an average accuracy of 98.09% for CN vs. MCI vs. AD classification and 95.16% for sMCI vs. pMCI classification. These results indicate that the proposed approach has significant potential to assist clinicians in developing targeted and personalized treatment plans.
Junxi Gao (Thu,) studied this question.