Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/111915
Title: A real-time clinical decision support system, for mild cognitive impairment detection, based on a hybrid neural architecture
Authors: Suárez Araujo, Carmen Paz 
García Báez, Patricio 
Cabrera León, Ylermi 
Prochazka, Ales
Rodríguez Espinosa, Norberto
Fernández Viadero, Carlos 
Editors: Bangyal, Waqas Haider
UNESCO Clasification: 3314 Tecnología médica
Issue Date: 2021
Project: Alzheimer’s Disease Neuroimaging Initiative (ADNI) U01 AG024904
DOD ADNI (Department of Defense award number W81XWH-12-2-0012)
Journal: Computational and Mathematical Methods in Medicine 
Abstract: Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and difficulties in understanding test cut-offs make diagnostic accuracy being low, particularly in primary care. Artificial neural networks (ANNs) are suitable to design computed aided diagnostic systems because of their features of generating relationships between variables and their learning capability. The main aim pursued in that work is to explore the ability of a hybrid ANN-based system in order to provide a tool to assist in the clinical decision-making that facilitates a reliable MCI estimate. The model is designed to work with variables usually available in primary care, including Minimental Status Examination (MMSE), Functional Assessment Questionnaire (FAQ), Geriatric Depression Scale (GDS), age, and years of education. It will be useful in any clinical setting. Other important goal of our study is to compare the diagnostic rendering of ANN-based system and clinical physicians. A sample of 128 MCI subjects and 203 controls was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The ANN-based system found the optimal variable combination, being AUC, sensitivity, specificity, and clinical utility index (CUI) calculated. The ANN results were compared with those from medical experts which include two family physicians, a neurologist, and a geriatrician. The optimal ANN model reached an AUC of 95.2%, with a sensitivity of 90.0% and a specificity of 84.78% and was based on MMSE, FAQ, and age inputs. As a whole, physician performance achieved a sensitivity of 46.66% and a specificity of 91.3%. CUIs were also better for the ANN model. The proposed ANN system reaches excellent diagnostic accuracy although it is based only on common clinical tests. These results suggest that the system is especially suitable for primary care implementation, aiding physicians work with cognitive impairment suspicions.
URI: http://hdl.handle.net/10553/111915
ISSN: 1748-670X
DOI: 10.1155/2021/5545297
Source: Computational and Mathematical Methods in Medicine [ISSN 1748-670X], v. 2021, 5545297
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