An interpretable multimodal ensemble assessment framework for Alzheimer's disease cognitive staging.

OBJECTIVES

Alzheimer's disease (AD) is the most prevalent type of neuro-degenerative dementia. Artificial intelligence assisted clinical evaluation can improve diagnostic efficiency and facilitate timely intervention.

METHODS

An interpretable Multimodal Ensemble Assessment Framework (MEAF) was developed to support clinical evaluation for AD across the cognitive spectrum. This framework employed a Swin Transformer and ResNet-50 for staged modeling of imaging features, applied machine learning techniques to extract clinical features, and used decision-level ensemble learning to integrate multimodal predictions. For interpretability analysis, Gradient-weighted Class Activation Maps were used to highlight key brain regions contributing to imaging-based decisions, and Shapley Additive exPlanations were applied to quantitatively assess the importance of clinical features.

RESULTS

MEAF achieved robust performance in classifying cognitively normal, mild cognitive impairment, and AD, with an accuracy of 0.878 and an F1-score of 0.877. In the independent external validation cohort, MEAF maintained reasonable performance, with an accuracy of 0.817 and an F1-score of 0.803. Interpretability analyses provided complementary explanations for both the imaging and clinical models.

CONCLUSION

MEAF demonstrated favorable classification performance and interpretability in retrospective multicenter datasets, suggesting its potential as an auxiliary framework for multimodal assessment of AD-related cognitive staging.