dc.contributor.author | Huang, Yo-Ping | |
dc.contributor.author | Zaza, Samuele M. M. | |
dc.contributor.author | Chu, Wen-Jang | |
dc.contributor.author | Krikorian, Robert | |
dc.contributor.author | Sandnes, Frode Eika | |
dc.date.accessioned | 2018-10-09T08:05:22Z | |
dc.date.accessioned | 2019-05-24T07:43:30Z | |
dc.date.available | 2018-10-09T08:05:22Z | |
dc.date.available | 2019-05-24T07:43:30Z | |
dc.date.issued | 2017-11-21 | |
dc.identifier.citation | Huang Y, Zaza, Chu, Krikorian, Sandnes FE. Using Fuzzy Systems to Infer Memory Impairment from MRI. International Journal of Fuzzy Systems. 2018;20(3):913-927 | en |
dc.identifier.issn | 1562-2479 | |
dc.identifier.issn | 1562-2479 | |
dc.identifier.uri | https://hdl.handle.net/10642/7158 | |
dc.description.abstract | Alzheimer’s Disease (AD) is a common form of dementia which mostly affects elderly people. Gradual
loss in memory and declining cognitive functions are core symptoms associated with AD. Conventional brain
images do not provide sufficient information to diagnose AD at an early stage. To delay the progression of memory
impairment there is a dire need to develop systems capable of early AD diagnosis. This paper describes a proposed
fuzzy method for inferring the risk of dementia using the brain cortical thickness and hippocampus thickness. The
aim is to develop a reliable index that allows the evaluation of brain health. The dementia index poses potential to
become a biologically-based biomarker for the clinical assessment of patient’s dementia. Results show that the
inference value of patient with mild cognitive impairment (MCI) is significantly higher than that of healthy
(Control) or schizophrenia (SCZ) patients. Our results suggest that a higher inference value indicates that the patient
is at higher risk and is more likely to eventually progress to AD. The system was also tested with age-associated
memory impairment (AAMI) patients. The results confirm that our model is able to distinguish between these four
patient groups. | en |
dc.description.sponsorship | This work was supported in part by the Ministry of Science and Technology, Taiwan under Grants MOST103
2221-E-027-122-MY2 and by a joint project between the National Taipei University of Technology and Mackay
Memorial Hospital under Grants NTUT-MMH-105-04 and NTUT-MMH-104-03. | en |
dc.language.iso | en | en |
dc.publisher | Taiwan Fuzzy Systems Association | en |
dc.publisher | Springer Verlag | en |
dc.relation.ispartofseries | International Journal of Fuzzy Systems;March 2018, Volume 20, Issue 3 | |
dc.rights | The final publication is available at Springer via http://dx.doi.org/10.1007/s40815-017-0417-8 | en |
dc.subject | Alzheimer’s disease | en |
dc.subject | Brain cortices | en |
dc.subject | Dementia risks | en |
dc.subject | Fuzzy systems | en |
dc.subject | Magnetic resonance imagings | en |
dc.title | Using Fuzzy Systems to Infer Memory Impairment from MRI | en |
dc.type | Journal article | en |
dc.type | Peer reviewed | en |
dc.date.updated | 2018-10-09T08:05:21Z | |
dc.description.version | acceptedVersion | en |
dc.identifier.doi | http://dx.doi.org/10.1007/s40815-017-0417-8 | |
dc.identifier.cristin | 1596912 | |
dc.source.journal | International Journal of Fuzzy Systems | |