Abstract
Nowadays, there is a population ageing which leads to an increasing of geriatric and non-communicable diseases. One of the major socio-sanitary challenges our society is facing is dementia, with Alzheimer’s disease (AD) as the most prevalent one. AD is a progressive neurodegenerative disorder over years, with several stages. One of them is the prodromal one, also called Mild Cognitive Impairment (MCI). Despite the recent advances in diagnostic criteria for AD, its definitive diagnosis is just possible post-mortem because there is nonspecific AD biomarker. Therefore, an early and differential diagnosis of AD is still an issue of high concern. Extensive research looking for appropriate methods of diagnosis has been done.
In this paper, we will present an innovative smart computing solution based on a hybrid and ontogenetic neural architecture, to deal with these challenges. It is an intelligent clinical decision-making system which has a non-neural pre-processing module and a neural processing one. This latter is a Modular Hybrid Growing Neural Gas (MyGNG), developed in this work. MyGNG consists of an input layer a Growing Neural Gas and a labelling layer based on the Perceptron algorithm. These modules are hierarchically organized and have different neurodynamic, connection topologies and learning laws.
Using just neuropsychological tests of 495 patients (150 AD, 345 MCI) from ADNI repository, our proposal has provided very promising results in the early detection of AD versus MCI, reaching values of AUC of 0.95; Sensitivity of 0.89 and Accuracy of 0.81. It is an appropriate diagnosis system for any clinical setting.
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_apply/ADNI_Acknowledgement_List.pdf.
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Acknowledgments
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 Ageing, the National Institute of Biomedical Imageing 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.
We would like to thank the anonymous reviewers for their valuable comments, which allowed improving the quality of the paper.
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Sosa-Marrero, A. et al. (2021). Detection of Alzheimer’s Disease Versus Mild Cognitive Impairment Using a New Modular Hybrid Neural Network. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12862. Springer, Cham. https://doi.org/10.1007/978-3-030-85099-9_18
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