Browsing by Subject "Neuroimaging biomarkers"

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    Author Correction: Predicting Alzheimer’s disease progression using multi-modal deep learning approach
    (Springer Nature, 2023-08-01) Lee, Garam; Nho, Kwangsik; Kang, Byungkon; Sohn, Kyung‑Ah; Kim, Dokyoon; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Correction to: Scientific Reports 10.1038/s41598-018-37769-z, published online 13 February 2019 This Article contains errors. A Supplementary Information file was omitted from the original version of this Article. The Supplementary Information file is now linked to this correction notice.
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    Predicting Alzheimer's disease progression using multi-modal deep learning approach
    (Springer Nature, 2019-02-13) Lee, Garam; Nho, Kwangsik; Kang, Byungkon; Sohn, Kyung-Ah; Kim, Dokyoon; Radiology and Imaging Sciences, School of Medicine
    Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.