Explainable Multimodal Deep Learning Framework for Early Alzheimer's Disease Detection Using MRI and Genomic Data

We presented EMDL-AD, an explainable multimodal deep learning framework for early Alzheimer's detection integrating structural MRI and genomic data. Through a novel cross-modal attention fusion mechanism and integrated explainability tools, the framework achieves superior accuracy (94.3%) while providing clinically interpretable predictions. Validation on two independent cohorts confirms generalizability, and ablation studies validate the contribution of each architectural component. By bridging high-performance AI with clinical interpretability, EMDL-AD represents a step toward trustworthy, personalized neurodegenerative disease diagnostics.  

1.                Scheltens, P., et al. (2021). Alzheimer's disease. The Lancet, 397(10284), 1577-1590. 2.                Jack, C. R., et al. (2018). NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimer's & Dementia, 14(4), 535-562. 3.                Sperling, R. A., et al. (2011). Toward defining the preclinical stages of Alzheimer's disease. Alzheimer's & Dementia, 7(3), 280-292. 4.                Karch, C. M., & Goate, A. M. (2015). Alzheimer's disease risk genes and mechanisms of disease pathogenesis. Biological Psychiatry, 77(1), 43-51. 5.                Wen, J., et al. (2020). Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation. Medical Image Analysis, 63, 101694. 6.                Escott-Price, V., et al. (2015). Polygenic risk score analysis of pathologically confirmed Alzheimer disease. Annals of Neurology, 78(2), 311-317. 7.                Tonekaboni, S., et al. (2019). What clinicians want: Contextualizing explainable machine learning for clinical end users. arXiv preprint arXiv:1905.05134. 8.                Suk, H. I., et al. (2017). Deep ensemble learning of sparse regression models for brain disease diagnosis. Medical Image Analysis, 37, 101-113. 9.                Liu, M., et al. (2020). Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE Transactions on Medical Imaging, 39(4), 1123-1134. 10.             Chouraki, V., et al. (2017). Evaluation of a genetic risk score to improve the prediction of Alzheimer's disease risk. Alzheimer's & Dementia: Translational Research & Clinical Interventions, 3(3), 399-408.