AI Article Synopsis
- The study focuses on differentiating mild cognitive impairment (MCI) from normal control (NC), crucial for early Alzheimer's disease (AD) detection and intervention.
- The researchers developed a new multi-modality supervised discriminative dictionary learning algorithm (mSCDDL) to analyze neuroimaging data from AD, MCI, and NC subjects.
- The results showed high classification accuracy (98.5% for AD vs. NC and 82.8% for MCI vs. NC), indicating that mSCDDL is an effective tool for early diagnosis using neuroimaging.
Article Abstract
Background And Objective: The differentiation of mild cognitive impairment (MCI), which is the prodromal stage of Alzheimer's disease (AD), from normal control (NC) is important as the recent research emphasis on early pre-clinical stage for possible disease abnormality identification, intervention and even possible prevention.
Methods: The current study puts forward a multi-modal supervised within-class-similarity discriminative dictionary learning algorithm (SCDDL) we introduced previously for distinguishing MCI from NC. The proposed new algorithm was based on weighted combination and named as multi-modality SCDDL (mSCDDL). Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET) and florbetapir PET data of 113 AD patients, 110 MCI patients and 117 NC subjects from the Alzheimer's disease Neuroimaging Initiative database were adopted for classification between MCI and NC, as well as between AD and NC.
Results: Adopting mSCDDL, the classification accuracy achieved 98.5% for AD vs. NC and 82.8% for MCI vs. NC, which were superior to or comparable with the results of some other state-of-the-art approaches as reported in recent multi-modality publications.
Conclusions: The mSCDDL procedure was a promising tool in assisting early diseases diagnosis using neuroimaging data.
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| http://dx.doi.org/10.1016/j.cmpb.2017.07.003 | DOI Listing |
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