TriFusion-ADFormer: a deep learning framework for early Alzheimer's disease detection using MRI and cognitive metrics.

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder with the gradual loss of cognitive functions and neuronal degeneration. Early and accurate diagnosis is essential for timely therapeutic intervention and improved patient management. However, effectively integrating complementary multimodal information for reliable AD classification remains a significant challenge. Methods: This study proposes TriFusion-ADFormer, a multimodal deep learning framework for multiclass classification of Alzheimer's disease (AD), mild cognitive impairment (MCI), and cognitively normal (CN) subjects. The framework extracts structural MRI features and MRI-derived clinical text summary based on volumetric measurements and cognitive assessment features such as MMSE, GDS, Global CDR, FAQ, and NPI-Q, then fuses them to classify the disease. Results: The proposed TriFusion-ADFormer achieved an overall classification accuracy of 86.0%, a Macro AUC of 0.93, and an F1-score of 86.0% for multiclass AD classification. Moreover, the MRI-based clinical summaries were also consistently consistent with structural abnormalities typically associated with AD, such as diffuse brain atrophy, which further supports the interpretability of the proposed framework. Discussion: The results show that the combination of multimodal information from structural MRI, semantic clinical summary generated from the MRI, and cognitive assessment scores enhances the accuracy and interpretability of Alzheimer's diagnosis. The results highlight the potential of incorporating complementary imaging, semantic, and cognitive features for better multiclass classification of AD, MCI, and CN in a transformer framework.