Cited 43 times in
Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation
DC Field | Value | Language |
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dc.contributor.author | 김창수 | - |
dc.contributor.author | 조한나 | - |
dc.date.accessioned | 2018-08-28T17:02:43Z | - |
dc.date.available | 2018-08-28T17:02:43Z | - |
dc.date.issued | 2018 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/162196 | - |
dc.description.abstract | To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject's cortical atrophy pattern with that of a representative AD patient cohort. In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia. Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively. In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p < 0.001) and first year visits (p < 0.001) relative to non-converters. Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p = 0.042), first year (p = 0.028), and third year visits (p = 0.027). The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.title | Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine | - |
dc.contributor.department | Dept. of Preventive Medicine | - |
dc.contributor.googleauthor | Jin San Lee | - |
dc.contributor.googleauthor | Changsoo Kim | - |
dc.contributor.googleauthor | Jeong-Hyeon Shin | - |
dc.contributor.googleauthor | Hanna Cho | - |
dc.contributor.googleauthor | Dae-Seock Shin | - |
dc.contributor.googleauthor | Nakyoung Kim | - |
dc.contributor.googleauthor | Hee Jin Kim | - |
dc.contributor.googleauthor | Yeshin Kim | - |
dc.contributor.googleauthor | Samuel N Lockhart | - |
dc.contributor.googleauthor | Duk L Na | - |
dc.contributor.googleauthor | Sang Won Seo | - |
dc.contributor.googleauthor | Joon-Kyung Seong | - |
dc.identifier.doi | 10.1038/s41598-018-22277-x | - |
dc.contributor.localId | A01042 | - |
dc.contributor.localId | A03920 | - |
dc.relation.journalcode | J02646 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.pmid | 29515131 | - |
dc.contributor.alternativeName | Kim, Chang Soo | - |
dc.contributor.alternativeName | Cho, Hanna | - |
dc.contributor.affiliatedAuthor | Kim, Chang Soo | - |
dc.contributor.affiliatedAuthor | Cho, Hanna | - |
dc.citation.volume | 8 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 4161 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, Vol.8(1) : 4161, 2018 | - |
dc.identifier.rimsid | 59782 | - |
dc.type.rims | ART | - |
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