Alzura: A Unified Ai-Ready Data Architecture for Comparative Research Across All Demantia Types
Keywords:
Dementia, AI-driven databases, ALZURA, FAIR principlesAbstract
Dementia such as Parkinson's disease, Alzheimer's disease, Lewy body dementia, frontotemporal dementia, and mixed forms show similar yet distinguishable pathological existence. Databases that are currently available are specific to each disease, fragmented, and do not have interconnectivity across datasets, which limits the comparative AI-driven research. This article introduces ALZURA, which is a unified AI-driven data framework built to integrate multi-domain datasets throughout major dementias. Engineered upon FAIR principles (Findable, Accessible, Interconnectable, Reusable), it integrates clinical imaging, biomarker and genomic data carefully into a systematized model. By allowing comparative analytics, ALZURA helps in fasten drug discovery, supports precision medicine, and promotes research on dementia in various healthcare contexts, including India. The suggested framework is authenticated against the repositories used currently (WHO, ADNI, PPMI, OASIS, NACC, and UK Biobank) and leads the datasets one step ahead. Future expansions are visioned to integrate ALZURA with real-world electronic healthcare records and affiliated learning systems.
Downloads
References
1. Jack, C. R., Jr., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., et al. (2008). The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4), 685–691. https://doi.org/10.1002/jmri.21049
2. Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Green, R. C., et al. (2015). The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimer's & Dementia, 11(6), e1–e120. https://doi.org/10.1016/j.jalz.2015.04.006
3. National Alzheimer's Coordinating Center. (2021). NACC Uniform Data Set (UDS) data dictionary (Version 3). University of Washington. https://doi.org/10.6069/VNTD-B258
4. Marek, K., Jennings, D., Lasch, S., Siderowf, A., Tanner, C., Simuni, T., et al. (2011). The Parkinson Progression Marker Initiative (PPMI). Progress in Neurobiology, 95(4), 629–635. https://doi.org/10.1016/j.pneurobio.2011.09.005
5. Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007). Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI data in young, middle-aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience, 19(9), 1498–1507. https://doi.org/10.1162/jocn.2007.19.9.1498
6. Marcus, D. S., Fotenos, A. F., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2010). Open Access Series of Imaging Studies (OASIS): Longitudinal MRI data in nondemented and demented older adults. Journal of Cognitive Neuroscience, 22(12), 2677–2684. https://doi.org/10.1162/jocn.2009.21407
7. LaMontagne, P. J., Benzinger, T. L. S., Morris, J. C., Keefe, S., Hornbeck, R., Xiong, C., et al. (2019). OASIS-3: Longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer's disease. medRxiv. https://doi.org/10.1101/2019.12.13.19014902
8. Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., et al. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature, 562(7726), 203–209. https://doi.org/10.1038/s41586-018-0579-z
9. Collins, R. (2012). What makes UK Biobank special? The Lancet, 379(9822), 1173–1174. https://doi.org/10.1016/S0140-6736(12)60404-8
10. World Health Organization. (2022). International classification of diseases for mortality and morbidity statistics (11th Revision). https://icd.who.int/ (Replace your current ICD-11 reference. The cited BMC paper is not the standard ICD-11 reference.)
11. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing. https://doi.org/10.1176/appi.books.9780890425596
12. Benson, T., & Grieve, G. (2021). Principles of health interoperability: SNOMED CT, HL7 and FHIR (4th ed.). Springer. https://doi.org/10.1007/978-3-030-56883-6
13. HL7 International. (2023). FHIR Release 5 (R5): Fast Healthcare Interoperability Resources Specification. https://hl7.org/fhir/
14. Rehm, H. L., Page, A. J. H., Smith, L., Adams, J. B., Alterovitz, G., Babb, L. J., et al. (2021). GA4GH: International policies and standards for data sharing across genomic research and healthcare. Cell Genomics, 1(2), 100029. https://doi.org/10.1016/j.xgen.2021.100029
15. Yates, A. D., Achuthan, P., Akanni, W., Allen, J., Alvarez-Jarreta, J., Amode, M. R., et al. (2021). Refget: Standardised access to reference sequences. Bioinformatics, 37(17), 2611–2613. https://doi.org/10.1093/bioinformatics/btab524
16. McDonald, C. J., Huff, S. M., Suico, J. G., et al. (2003). LOINC, a universal standard for identifying laboratory observations: A 5-year update. Clinical Chemistry, 49(4), 624–633. https://doi.org/10.1373/49.4.624
17. Nelson, S. J., Zeng, K., Kilbourne, J., Powell, T., & Moore, R. (2011). Normalized names for clinical drugs: RxNorm at 6 years. Journal of the American Medical Informatics Association, 18(4), 441–448. https://doi.org/10.1136/amiajnl-2011-000116
18. International Test Commission. (2017). The ITC guidelines for translating and adapting tests (2nd ed.). International Journal of Testing, 18(2), 101–134. https://doi.org/10.1080/15305058.2017.1398166
19. World Health Organization. (2020). Global dementia observatory survey: A benchmark for national dementia policies and health systems. Bulletin of the World Health Organization, 98(10), 653–660. https://doi.org/10.2471/BLT.20.253815
20. Fiume, M., Cupak, M., Keenan, S., Rambla, J., de la Torre, S., Dyke, S. O., et al. (2019). Federated discovery and sharing of genomic data using GA4GH standards. Cell Systems, 9(2), 123–127. https://doi.org/10.1016/j.cels.2019.07.007
