2024 JSM Proceedings
Published October 31, 2024 | Version v1
Conference paper Open

Multimodal Machine Learning Algorithm as a Tool for Dementia Clinical Trial Screening

Creators

  • 1. West Windsor-Plainsboro High School North
  • 2. ROR icon National University of Kyiv Mohyla Academy
  • 3. Proof School
  • 4. ROR icon National Renewable Energy Laboratory
  • 5. NeuroClin
  • 6. Princeton Pharmatech LLC

Description

Dementia is a complex disease due to various etiologies. However, new multimodal machine learning algorithms were developed to improve the diagnosis of dementia into different categories, including normal cognition (NC), mild cognitive impairment (MCI), Alzheimer’s disease (AD), and non-AD dementias (nADD). One of the core difficulties in implementing dementia clinical trials, especially the AD trials lies in the diagnostic ambiguity of Alzheimer's, where symptomatic overlap with other cognitive disorders often leads to misdiagnosis. There are usually high screen failure rates in dementia clinical trials, which is a burden for the sponsor due to the manual verification of the screening that hinders the process. In this study, we aimed to explore the application of a multimodal machine learning algorithm as a tool for clinical trial patients’ disease screening verification to reduce the cost of the clinical study by improving its quality, accessibility, and speed. The accuracy assessment of the machine learning algorithm will be presented by comparing its results to a neurologist’s evaluation. The results will be described using the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) in a real-world clinical trial setting. We will explore the optimal set of input variables used for the algorithm to balance the medical exams' accuracy, cost, and time.

Files

JSM_2024_Paper.pdf

Files (2.2 MB)

Name Size Download all
md5:f14aba0a0c5059c09b3727c518b820eb
2.2 MB Preview Download

Additional details

References

  • Aerqin, Q., Wang, Z. T., Wu, K., He, X., Dong, Q., & Yu, J. (2022, November 8). Omics-based biomarkers discovery for Alzheimer's disease. Cellular and Molecular Life Sciences. https://doi.org/10.1007/s00018-022-04614-6
  • Badhwar, A., McFall, G. P., Sapkota, S., Black, S. E., Chertkow, H., Duchesne, S., Masellis, M., Liang, L., Dixon, R. A., & Bellec, P. (2019, December 13). A multiomics approach to heterogeneity in Alzheimer's disease: focused review and roadmap. Brain. https://doi.org/10.1093/brain/awz384
  • Bai, B., Vanderwall, D., Li, Y., Wang, X., Suresh, P., Wang, H., Dey, K., Chen, P., Yang, K., & Peng, J. (2021, August 12). Proteomic landscape of Alzheimer's Disease: novel insights into pathogenesis and biomarker discovery. Molecular Neurodegeneration. https://doi.org/10.1186/s13024-021-00474-z
  • Balsis, S., Benge, J. F., Lowe, D. A., Geraci, L., & Doody, R. S. (2015, October 3). How Do Scores on the ADAS-Cog, MMSE, and CDR-SOB Correspond? The Clinical Neuropsychologist. https://doi.org/10.1080/13854046.2015.1119312
  • Blennow, K., De Leon, M. J., & Zetterberg, H. (2006, July 1). Alzheimer's disease. The Lancet. https://doi.org/10.1016/s0140-6736(06)69113-7
  • Budelier, M. M., & Bateman, R. J. (2019, December 30). Biomarkers of Alzheimer Disease. The Journal of Applied Laboratory Medicine. https://doi.org/10.1373/jalm.2019.030080
  • Chang, C., Lin, C., & Lane, H. Y. (2021, March 9). Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer's Disease. International Journal of Molecular Sciences. https://doi.org/10.3390/ijms22052761
  • [Epidemiology of Alzheimer's disease: latest trends]. (2020, February 1). PubMed.
  • El–Sappagh, S., Alonso, J. M., Islam, S. M. R., Sultan, A., & Kwak, K. S. (2021, January 29). A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease. Scientific Reports. https://doi.org/10.1038/s41598-021- 82098-3
  • Geldmacher, D. S., & Whitehouse, P. J. (1996). Evaluation of Dementia. In New England Journal of Medicine (Vol. 335, Issue 5, pp. 330–336). Massachusetts Medical Society. https://doi.org/10.1056/nejm199608013350507
  • Hendrie, H. C. (1998, March 1). Epidemiology of Dementia and Alzheimer's Disease. The American Journal of Geriatric Psychiatry. https://doi.org/10.1097/00019442-199821001-00002
  • Hurtado, M. O., Köhler, I., & De Lange, E. C. M. (2018, September 1). Next-generation biomarker discovery in Alzheimer's disease using metabolomics – from animal to human studies. Bioanalysis. https://doi.org/10.4155/bio-2018-0135
  • Jellinger, K. A. (2007, January 1). The enigma of mixed dementia. Alzheimer's & Dementia. https://doi.org/10.1016/j.jalz.2006.09.002
  • Alzheimer's Association. (2022). Mixed dementia. https://www.alz.org/media/Documents/alzheimers-dementia-mixed_dementia-ts.pdf
  • Kalaria, R. N. (2002, November 1). Similarities between Alzheimer's disease and vascular dementia. Journal of the Neurological Sciences. https://doi.org/10.1016/s0022-510x(02)00256- 3
  • Karako, K., Song, P., & Yu, C. (2023, February 28). Recent deep learning models for dementia as point-of-care testing: Potential for early detection. Intractable & Rare Diseases Research. https://doi.org/10.5582/irdr.2023.01015
  • McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., Mohs, R. C., Morris, J. C., Rossor, M. N., Scheltens, P., Carrillo, M. C., Thies, B., Weıntraub, S., & Phelps, C. H. (2011, April 22). The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's & Dementia. https://doi.org/10.1016/j.jalz.2011.03.005
  • Meek, P. D., McKeithan, E. K., & Schumock, G. T. (1998, March 4). Economic Considerations in Alzheimer's Disease. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy. https://doi.org/10.1002/j.1875-9114.1998.tb03880.x
  • Passeri, E., Elkhoury, K., Morsink, M., Broersen, K., Linder, M., Tamayol, A., Malaplate, C., Yen, F. T., & Arab‐Tehrany, E. (2022, November 12). Alzheimer's Disease: Treatment Strategies and Their Limitations. International Journal of Molecular Sciences. https://doi.org/10.3390/ijms232213954
  • Scheltens, P., De Strooper, B., Kivipelto, M., Holstege, H., Chételat, G., Teunissen, C. E., Cummings, J. L., & Van Der Flier, W. M. (2021, April 1). Alzheimer's disease. The Lancet. https://doi.org/10.1016/s0140-6736(20)32205-4
  • Senanarong, V. (2021). Application of Artificial Intelligence-Machine Learning in Dementia. http://dx.doi.org/10.31031/sbb.2021.05.000608
  • Skolariki, K., & Exarchos, T. P. (2020, January 1). Computational Approaches Applied in the Field of Neuroscience. Advances in Experimental Medicine and Biology. https://doi.org/10.1007/978-3-030-32622-7_17
  • Tan, M. S., Cheah, P., Chin, A., Looi, L., & Chang, S. W. (2021, December 1). A review on omicsbased biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2021.104947
  • Vermeiren, Y., Van Dam, D., De Vries, M., & De Deyn, P. P. (2020, December 15). Psychiatric Disorders in Dementia. Springer eBooks. https://doi.org/10.1007/978-3-030-57231-0_9
  • Walhovd, K. B., Fjell, A. M., Brewer, J. B., McEvoy, L. K., Fennema-Notestine, C., Hagler, D. J., Jennings, R. G., Karow, D. S., & Dale, A. M. (2010, January 14). Combining MR Imaging, Positron-Emission Tomography, and CSF Biomarkers in the Diagnosis and Prognosis of Alzheimer Disease. American Journal of Neuroradiology. https://doi.org/10.3174/ajnr.a1809
  • Jack, C. R., Andrews, J. S., Beach, T. G., Buracchio, T., Dunn, B., Graf, A., Hansson, O., Ho, C., Jagust, W., McDade, E., Molinuevo, J. L., Okonkwo, O. C., Pani, L., Rafii, M. S., Scheltens, P., Siemers, E., Snyder, H. M., Sperling, R., Teunissen, C. E., & Carrillo, M. C. (2024). Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup. Alzheimer S & Dementia. https://doi.org/10.1002/alz.13859
  • World Health Organization. (2019). Dementia. In International statistical classification of diseases and related health problems (11th ed.). https://icd.who.int/browse10/2019/en#/F00-F09
  • Zetterberg, H., & Blennow, K. (2021, February 19). Moving fluid biomarkers for Alzheimer's disease from research tools to routine clinical diagnostics. Molecular Neurodegeneration. https://doi.org/10.1186/s13024-021-00430-x
  • Wong‐Lin, K., McClean, P., McCombe, N., Kaur, D., Sánchez-Bornot, J. M., Gillespie, P., Todd, S., Finn, D. P., Joshi, A., Kane, J., & McGuinness, B. (2020, December 1). Shaping a datadriven era in dementia care pathway through computational neurology approaches. BMC Medicine. https://doi.org/10.1186/s12916-020-01841-1
  • Wong‐Lin, K., McClean, P., McCombe, N., Kaur, D., Sánchez-Bornot, J. M., Gillespie, P., Todd, S., Finn, D. P., Joshi, A., Kane, J., & McGuinness, B. (2020, December 1). Shaping a datadriven era in dementia care pathway through computational neurology approaches. BMC Medicine. https://doi.org/10.1186/s12916-020-01841-1
  • Xie, L., He, B., Varathan, P., Nho, K., Risacher, S. L., Saykin, A. J., Salama, P., & Yan, J. (2021, May 10). Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer's disease. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbab121