Multimodal AI for Early Alzheimer's Biomarker Discovery
Keywords:
Alzheimer’s Disease Dementia, COVID-19–Related Neurocognitive Disorders, Neurodegeneration Risk Factors, Pandemic-Induced Social Isolation, Early Detection of Alzheimer’s Disease, Disease-Modifying Therapies, Multimodal Biomarkers, Artificial Intelligence in Neurology, AI-Driven Biomarker Discovery, Neurocognitive Decline Prediction, Preclinical Alzheimer’s Identification, Longitudinal Disease Risk Assessment, Predictive Modeling for Dementia, State-of-the-Art AI Tools in Healthcare, Marker-Driven Therapeutic Interventions, Neurocognitive Outcome Prediction, Multimodal Data Integration, Precision Medicine for Neurodegeneration, Early-Stage Pathogenesis Assessment, AI-Supported Clinical Decision Making.Abstract
Covid-19 (Covid) pandemic-exacerbated physical and social isolation are recognized as risk factors for the subsequent triggering or aggravation of neurocognitive disorders. Indeed, there has been an alarming 25% increase in Alzheimer’s disease (AD) dementia-associated deaths that may be attributable to Covid. Despite this increasing mortality with AD-related dementia and other neurodegeneration disorders resulting from Covid infection, breaking news continues to provide evidence supporting the observed neurocognitive behaviours, such as poorer memory and judgment and slower processing. With more than 6 million people in the U.S. living with AD, it is crucial to establish disease-modifying therapies that target the earliest possible stages of the disease. Prescription of disease-modifying therapies requires early identification of patients who are likely to proceed toward manifest dementia, and the identification of multimodal biomarkers using state-of-the-art AI tools could facilitate this process. Although these multimodal markers can ultimately predict asymptomatic individuals who are likely to develop AD dementia at a specific time in the future, such as within 2 years, 5 years, or 10 years, the predictor in different studies is not conceptually identical. As a result, the precisely defined prediction would be expected to best suit a disease-modifying therapy or intervention.
Currently, the detection of the origin of AD pathogenesis remains elusive, and it is still unknown what the first or earliest marker is that appears after the disease pathology starts. Nevertheless, multimodal biomarkers are essential to assess disease risk. Support from state-of-the-art artificial intelligence tools would contribute to the identification of multimodal biomarkers that have the potential for early detection and development of a marker-driven disease-modifying therapy.
References
1. Venugopalan, J., Tong, L., Hassanzadeh, H. R., & Wang, M. D. (2021). Multimodal deep learning models for early detection of Alzheimer’s disease stage. Scientific Reports, 11, 3254.
2. Song, J., Zheng, J., Li, P., Lu, X., Zhu, G., & Shen, P. (2021). An effective multimodal image fusion method using MRI and PET for Alzheimer’s disease diagnosis. Frontiers in Digital Health, 3, 637386.
3. Ganesh, S. K., Subbareddy, K., Gopi, A., Davuluri, P. N., Mannar, B. R., & Karnawat, A. T. (2026, June). Fraudulent Credit Card Transaction Detection Using a Hybrid Ensemble-Anomaly Detection Framework. In 2026 6th International Conference on Intelligent Technologies (CONIT) (pp. 1-6). IEEE.
4. Ashton, N. J., Pascoal, T. A., Karikari, T. K., Benedet, A. L., Lantero-Rodriguez, J., Snellman, A., Schöll, M., Troakes, C., Hye, A., Gauthier, S., Vanmechelen, E., Zetterberg, H., Rosa-Neto, P., & Blennow, K. (2021). Plasma p-tau231: A new biomarker for incipient Alzheimer’s disease pathology. Acta Neuropathologica, 141, 709–724.
5. Janelidze, S., Berron, D., Smith, R., Strandberg, O., Proctor, N. K., Dage, J. L., Stomrud, E., Ashton, N. J., Blennow, K., & Hansson, O. (2021). Associations of plasma phospho-tau217 levels with tau positron emission tomography in early Alzheimer disease. JAMA Neurology, 78(2), 149–156.
6. Yandamuri, U. S., Loganathan, R., Davuluri, P. S. L. N., Rani, P. R. S., Kolla, S. H., & Nagubandi, A. R. (2026). Adaptive Intelligence Networks for Humancentered Enterprise Automation and Governance. In 2026 Third International Conference on Innovations in Cybersecurity and Data Science (ICICDS) (pp. 678–683). IEEE. 2026 Third International Conference on Innovations in Cybersecurity and Data Science (ICICDS). https://doi.org/10.1109/icicds70526.2026.11604640
7. Qiu, S., Miller, M. I., Joshi, P. S., Lee, J. C., Xue, C., Ni, Y., Wang, Y., De Anda-Duran, I., Hwang, P. H., Cramer, J. A., Dwyer, B. C., Hao, H., & Kolachalama, V. B. (2022). Multimodal deep learning for Alzheimer’s disease dementia assessment. Nature Communications, 13, 3404.
8. Houria, L., Belkhamsa, N., Cherfa, A., & Cherfa, Y. (2022). Multi-modality MRI for Alzheimer’s disease detection using deep learning. Physical and Engineering Sciences in Medicine, 45(4), 1043–1053.
9. Banoth, S., Santoshi Kumari, M., Lalitha, P., Deepthi, P., Kumar Peddi, R., & Kavitha, P. (2026). An Efficient Hybrid K-Means and Random Forest-Based Approach for Cloud Malware Detection and Privacy Protection. In 2026 6th International Conference on Intelligent Technologies (CONIT) (pp. 1–6). IEEE. 2026 6th International Conference on Intelligent Technologies (CONIT). https://doi.org/10.1109/conit69683.2026.11621822
10. Suárez-Calvet, M., Karikari, T. K., Ashton, N. J., Fandos, N., Turon-Sans, J., Grøntvedt, G. R., et al. (2022). Plasma p-tau231 and p-tau217 as state markers of amyloid-β pathology in preclinical Alzheimer’s disease. Nature Medicine, 28, 1797–1801.
11. Jack, C. R., Jr., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., Liu, E., Molinuevo, J. L., Montine, T., Phelps, C., Rankin, K. P., Rowe, C. C., Scheltens, P., Siemers, E., Snyder, H. M., & Silverberg, N. (2021). NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia, 17(5), 669–680.
12. Hansson, O. (2021). Biomarkers for neurodegenerative diseases. Nature Medicine, 27, 954–963.
13. Mangalampalli, B. M., Peddi, R. K., Kolla, S. K., Reddy, V. A. R., Mangala, N., & Seenu, A. (2026, June). Explainable Clinical Graph Intelligence Framework for Longitudinal Risk Modeling and Care Pathway Optimization. In 2026 Third International Conference on Innovations in Cybersecurity and Data Science (ICICDS) (pp. 1-6). IEEE.
14. Teunissen, C. E., Verberk, I. M. W., Thijssen, E. H., Vermunt, L., Hansson, O., Zetterberg, H., & van der Flier, W. M. (2022). Blood-based biomarkers for Alzheimer’s disease: Towards clinical implementation and diagnostic utility. Nature Reviews Neurology, 18, 545–559.
15. Ashton, N. J., Janelidze, S., Al Khleifat, A., et al. (2022). A multicentre validation study of the diagnostic value of plasma neurofilament light and phosphorylated tau in Alzheimer’s disease. Nature Communications, 13.
16. Mielke, M. M., & Fowler, N. R. (2024). Alzheimer disease blood biomarkers: Considerations for population-level use. Nature Reviews Neurology, 20, 495–504.
17. Ashton, N. J., Brum, W. S., Di Molfetta, G., et al. (2024). Diagnostic accuracy of a plasma phosphorylated tau 217 immunoassay for Alzheimer disease pathology. JAMA Neurology, 81(3), 255–263.
18. Mahadevan, S. (2026). Governed Serverless Automation Ecosystem for AI-Driven Enterprise Integration and Sustainable Cloud Operations. International Journal of Computer Information Systems and Industrial Management Applications, 18(16s), 1561-1570.
19. Palmqvist, S., Tideman, P., Mattsson-Carlgren, N., et al. (2024). Blood biomarkers to detect Alzheimer disease in primary care and secondary care. JAMA, 332(15), 1245–1257.
20. Janelidze, S., Mattsson-Carlgren, N., Palmqvist, S., et al. (2022). Plasma biomarkers in Alzheimer’s disease: Association with amyloid and tau pathology and diagnostic performance. Alzheimer’s & Dementia, 18.
21. Karikari, T. K., Pascoal, T. A., Ashton, N. J., Janelidze, S., Benedet, A. L., Rodriguez, J. L., et al. (2021). Blood phosphorylated tau 181 as a biomarker for Alzheimer’s disease. Nature Reviews Neurology, 17, 69–70.
22. Vakkalagadda, T., & Rajamanickam, V. (2026). Agentic AI Architectures for Next-Generation Investment Advisory and Portfolio Intelligence. International Journal of Computer Information Systems and Industrial Management Applications, 18(9s), 1206-1219.
23. Benedet, A. L., Leuzy, A., Pascoal, T. A., Ashton, N. J., Mathotaarachchi, S., Rodriguez, J. L., et al. (2021). Stage-specific links between plasma and cerebrospinal fluid phosphorylated tau in Alzheimer’s disease. Nature Medicine, 27.
24. Thijssen, E. H., La Joie, R., Wolf, A., Strom, A., Wang, P., Iaccarino, L., et al. (2021). Diagnostic value of plasma phosphorylated tau181 in Alzheimer’s disease and frontotemporal lobar degeneration. Nature Medicine, 27, 1034–1042.
25. Palmqvist, S., Janelidze, S., Stomrud, E., et al. (2021). Performance of fully automated plasma assays as screening tests for Alzheimer disease-related β-amyloid pathology. JAMA Neurology, 78.
26. Piyush Kumar Pareek. (2026). Human-Centric Machine Learning Frameworks for Scalable Software Quality Prediction. Journal of Intelligent Decision Making and Information Science, 3(5s), 1525–1543. https://doi.org/10.59543/jidmis.v3.1284
27. Tissot, C., Therriault, J., Kunz, L., et al. (2022). Comparing plasma phospho-tau217 and phospho-tau181 with amyloid PET and tau PET in Alzheimer’s disease. Alzheimer’s & Dementia, 18.
28. Leuzy, A., Chiotis, K., Lemoine, L., Gillberg, P. G., Almkvist, O., Rodriguez-Vieitez, E., & Nordberg, A. (2021). Tau PET imaging in neurodegenerative tauopathies—Still a challenge. Molecular Psychiatry, 26.
29. Barthelemy, N. R., Li, Y., Joseph-Mathurin, N., et al. (2022). A soluble phosphorylated tau signature links tau, amyloid and neurodegeneration in Alzheimer’s disease. Nature Medicine, 28.
30. Bhavani, B. D., SR, S., Loganathan, R., & Nagaraj, S. (2026, April). Evolutionary Gravitational Neocognitron Neural Network, Snow Leopard Optimization and Deep Graph Reinforcement Learning for Routing Protocol in WSN. In 2026 2nd International Conference on Intelligent Systems and Computational Networks (ICISCN) (pp. 1-8). IEEE.
31. Palmqvist, S., Stomrud, E., Cullen, N., et al. (2023). An accurate fully automated panel of plasma biomarkers for Alzheimer’s disease. Alzheimer’s & Dementia, 19.
32. Hansson, O., Blennow, K., Zetterberg, H., & Dage, J. L. (2023). Blood biomarkers for Alzheimer’s disease in clinical practice and research. Nature Reviews Neurology, 19.
33. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).
34. Odusami, M., Maskeliūnas, R., Damaševičius, R., & Misra, S. (2023). Explainable deep-learning-based diagnosis of Alzheimer’s disease using multimodal input fusion of PET and MRI images. Journal of Medical and Biological Engineering, 43, 291–302.
35. Automated detection of Alzheimer’s disease: A multi-modal approach with 3D MRI and amyloid PET. (2024). Scientific Reports.
36. Piyush Kumar Pareek. (2026). Self-Evolving Analytics Pipelines for Reliable AI-Augmented Software Systems. Journal of Intelligent Decision Making and Information Science, 3(5s), 1558–1578. https://doi.org/10.59543/jidmis.v3.1286
37. Multi-scale multimodal deep learning framework for Alzheimer’s disease diagnosis. (2024). Computers in Biology and Medicine, 181, 109438.
38. Stomrud, E., Palmqvist, S., Blennow, K., et al. (2024). A blood-based biomarker strategy for identifying Alzheimer’s disease pathology in clinical practice. Nature Medicine, 30, 1085–1095.
39. Salvadó, G., Janelidze, S., Bali, D., et al. (2025). Plasma phosphorylated tau 217 to identify preclinical Alzheimer disease. JAMA Neurology, 82(11), 1122–1134.
40. Loganathan, R. (2026). SaaS Entitlement Governance: A Reproducible Model for Detecting and Reclaiming Over-Provisioned Access at Enterprise Scale. Journal of Intelligent Decision Making and Information Science, 3(7s), 2519-2530.
41. A review of multimodal fusion-based deep learning for Alzheimer’s disease. (2025). Neuroscience, 576, 80–95.
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Data Availability Statement
Data used in this study were derived from the publicly available ADNI dataset, governed by the ADNI Data Usage Agreement
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Copyright (c) 2026 Sophia Martinez (Author)
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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
