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• W2807346720 abstract "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." @default.
• W2807346720 created "2018-06-13" @default.
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• W2807346720 date "2018-03-07" @default.
• W2807346720 modified "2023-10-13" @default.
• W2807346720 title "Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer’s Disease Spectrum: Development of the Classifier and Longitudinal Evaluation" @default.
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• W2807346720 doi "https://doi.org/10.1038/s41598-018-22277-x" @default.
• W2807346720 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5841386" @default.
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• W2807346720 hasPublicationYear "2018" @default.
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