Abstract
The use of deep learning in diagnostic and modeling the progression of neurodegenerative diseases has had a significant boom in the last years. The model complexity, due to the large quantity and diversity of data necessary for their training, do not make them affordable in conditions where obtaining clinical data and magnetic resonance imaging are expensive and complex. However, under these conditions it is feasible using scores from cognitive functions. These techniques are cheap and do not require the use of sophisticated equipment. In this work we propose a deep learning based model for classification of cognitive vectors collected from each patient taking into account the labels corresponding to the disease stage (normal, mild cognitive impairment, and diseased). Experiments on ADNI cohorts shown that our proposal maintained an average accuracy of 0.968 with a standard deviation of 0.01, which is higher than the obtained by the compared methods. The experiments demonstrated the feasibility of the proposed model.
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Notes
- 1.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_aply/ADNI_Acknowledgement_List.pdf.
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Acknowledgement
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12–2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
This work was also founded by the Cuban Center for Neurosciences through project PN305LH013-015. Development of disease progression models for brain dysfunctions.
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Garea-Llano, E., León Pino, S., Martinez-Montes, E. (2024). Diagnosis of Alzheimer Disease Progression Stage from Cross Sectional Cognitive Data by Deep Neural Network. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2023. Lecture Notes in Computer Science, vol 14335. Springer, Cham. https://doi.org/10.1007/978-3-031-49552-6_24
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