-
Je něco špatně v tomto záznamu ?
A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture
CP. Suárez-Araujo, P. García Báez, Y. Cabrera-León, A. Prochazka, N. Rodríguez Espinosa, C. Fernández Viadero, FTAD. Neuroimaging Initiative
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články
NLK
Free Medical Journals
od 2011
PubMed Central
od 2011
Europe PubMed Central
od 2011
Open Access Digital Library
od 1997-01-01
Open Access Digital Library
od 2006-01-01
Open Access Digital Library
od 2011-01-01
Medline Complete (EBSCOhost)
od 2006-03-01 do 2023-06-29
Wiley-Blackwell Open Access Titles
od 1997
PubMed
34257699
DOI
10.1155/2021/5545297
Knihovny.cz E-zdroje
- MeSH
- databáze faktografické statistika a číselné údaje MeSH
- diagnóza počítačová metody statistika a číselné údaje MeSH
- kognitivní dysfunkce diagnóza psychologie MeSH
- lidé MeSH
- neuronové sítě * MeSH
- neuropsychologické testy * statistika a číselné údaje MeSH
- plocha pod křivkou MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- senzitivita a specificita MeSH
- studie případů a kontrol MeSH
- systémy pro podporu klinického rozhodování * statistika a číselné údaje MeSH
- výpočetní biologie MeSH
- Check Tag
- lidé MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- Publikační typ
- časopisecké články MeSH
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.
Departamento de Ingeniería Informática y de Sistemas Universidad de La Laguna La Laguna Spain
Servicio de Psiquiatría Hospital Universitario Marqués de Valdecilla Santander Spain
Citace poskytuje Crossref.org
- 000
- 00000naa a2200000 a 4500
- 001
- bmc22004202
- 003
- CZ-PrNML
- 005
- 20220127145436.0
- 007
- ta
- 008
- 220113s2021 xxu f 000 0|eng||
- 009
- AR
- 024 7_
- $a 10.1155/2021/5545297 $2 doi
- 035 __
- $a (PubMed)34257699
- 040 __
- $a ABA008 $b cze $d ABA008 $e AACR2
- 041 0_
- $a eng
- 044 __
- $a xxu
- 100 1_
- $a Suárez-Araujo, Carmen Paz $u Instituto Universitario de Ciencias y Tecnologías Cibernéticas, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
- 245 12
- $a A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture / $c CP. Suárez-Araujo, P. García Báez, Y. Cabrera-León, A. Prochazka, N. Rodríguez Espinosa, C. Fernández Viadero, FTAD. Neuroimaging Initiative
- 520 9_
- $a 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.
- 650 _2
- $a senioři $7 D000368
- 650 _2
- $a senioři nad 80 let $7 D000369
- 650 _2
- $a plocha pod křivkou $7 D019540
- 650 _2
- $a studie případů a kontrol $7 D016022
- 650 _2
- $a kognitivní dysfunkce $x diagnóza $x psychologie $7 D060825
- 650 _2
- $a výpočetní biologie $7 D019295
- 650 _2
- $a databáze faktografické $x statistika a číselné údaje $7 D016208
- 650 12
- $a systémy pro podporu klinického rozhodování $x statistika a číselné údaje $7 D020000
- 650 _2
- $a diagnóza počítačová $x metody $x statistika a číselné údaje $7 D003936
- 650 _2
- $a lidé $7 D006801
- 650 12
- $a neuronové sítě $7 D016571
- 650 12
- $a neuropsychologické testy $x statistika a číselné údaje $7 D009483
- 650 _2
- $a senzitivita a specificita $7 D012680
- 655 _2
- $a časopisecké články $7 D016428
- 700 1_
- $a García Báez, Patricio $u Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, La Laguna, Spain
- 700 1_
- $a Cabrera-León, Ylermi $u Instituto Universitario de Ciencias y Tecnologías Cibernéticas, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
- 700 1_
- $a Prochazka, Ales $u Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, Prague, Czech Republic $u Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, Czech Republic
- 700 1_
- $a Rodríguez Espinosa, Norberto $u Unidad de Neurología de la Conducta y Memoria, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
- 700 1_
- $a Fernández Viadero, Carlos $u Servicio de Psiquiatría, Hospital Universitario Marqués de Valdecilla, Santander, Spain
- 700 1_
- $a Neuroimaging Initiative, For The Alzheimer's Disease $u Center for Imaging of Neurodegenerative Disease San Francisco VA Medical Center University of California, San Francisco, USA
- 773 0_
- $w MED00173439 $t Computational and mathematical methods in medicine $x 1748-6718 $g Roč. 2021, č. - (2021), s. 5545297
- 856 41
- $u https://pubmed.ncbi.nlm.nih.gov/34257699 $y Pubmed
- 910 __
- $a ABA008 $b sig $c sign $y p $z 0
- 990 __
- $a 20220113 $b ABA008
- 991 __
- $a 20220127145433 $b ABA008
- 999 __
- $a ok $b bmc $g 1751607 $s 1155351
- BAS __
- $a 3
- BAS __
- $a PreBMC
- BMC __
- $a 2021 $b 2021 $c - $d 5545297 $e 20210621 $i 1748-6718 $m Computational and mathematical methods in medicine $n Comput Math Methods Med $x MED00173439
- LZP __
- $a Pubmed-20220113