TY - GEN
T1 - Fuzzy Clustering to Asses BALI and LIBRA Factors for Estimation of DTI Measures
AU - Akbarifar, Ahmad
AU - Maghsoudpour, Adel
AU - Mohammadian, Fatemeh
AU - Mohammadzaheri, Morteza
AU - Ghaemi, Omid
N1 - Publisher Copyright:
© 2023 IEEE.
DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2023/8/30
Y1 - 2023/8/30
N2 - Diffusion magnetic resonance imaging (dMRI) is a popular technique for diagnosing dementia through finding a number of measures with diffusion tensor imaging (DTI). However, this technique is too expensive to be widely used to scan populations. The primary objective of this research is to identify factors/indices which are both (i) rather inexpensive to find, and (ii) usable to estimate DTI measures and eventually to diagnose dementia. This will the basis for a low-cost diagnostic solution. Such factors are selected amongst lifestyle for brain health (LIBRA) and brain atrophy and lesion index (BALI) factors. These factors are pertinent to dementia and relatively inexpensive to find. However, BALI and LIBRA are comprised of 49 factors altogether, and development of a diagnostic algorithm with 49 inputs is infeasible. Therefore, it is necessary to pick the most impactful factors to be used in diagnosis algorithm development. Fuzzy subtractive clustering was employed for this purpose. This research shows that the grey matter lesions and subcortical dilated perivascular spaces (GM-SV) and periventricular white matter lesions (PV) from BALI and age, level of education, job status, antidepressant drugs, diabetes control drugs, obesity (BMI) and dementia preventive diet from LIBRA are the most influential factors to identify DTI measures.
AB - Diffusion magnetic resonance imaging (dMRI) is a popular technique for diagnosing dementia through finding a number of measures with diffusion tensor imaging (DTI). However, this technique is too expensive to be widely used to scan populations. The primary objective of this research is to identify factors/indices which are both (i) rather inexpensive to find, and (ii) usable to estimate DTI measures and eventually to diagnose dementia. This will the basis for a low-cost diagnostic solution. Such factors are selected amongst lifestyle for brain health (LIBRA) and brain atrophy and lesion index (BALI) factors. These factors are pertinent to dementia and relatively inexpensive to find. However, BALI and LIBRA are comprised of 49 factors altogether, and development of a diagnostic algorithm with 49 inputs is infeasible. Therefore, it is necessary to pick the most impactful factors to be used in diagnosis algorithm development. Fuzzy subtractive clustering was employed for this purpose. This research shows that the grey matter lesions and subcortical dilated perivascular spaces (GM-SV) and periventricular white matter lesions (PV) from BALI and age, level of education, job status, antidepressant drugs, diabetes control drugs, obesity (BMI) and dementia preventive diet from LIBRA are the most influential factors to identify DTI measures.
KW - BALI
KW - DTI
KW - Dementia
KW - Diffusion MRI
KW - Fuzzy subtractive clustering
KW - LIBRA
UR - http://www.scopus.com/inward/record.url?scp=85175576414&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85175576414&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/6a302948-f86e-369f-b363-4de6fad5cd87/
U2 - 10.1109/icac57885.2023.10275298
DO - 10.1109/icac57885.2023.10275298
M3 - Conference contribution
AN - SCOPUS:85175576414
SN - 9798350335859
T3 - ICAC 2023 - 28th International Conference on Automation and Computing
SP - 1
EP - 6
BT - ICAC 2023 - 28th International Conference on Automation and Computing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th International Conference on Automation and Computing, ICAC 2023
Y2 - 30 August 2023 through 1 September 2023
ER -
