Journal article
Predicting Alzheimer's disease progression using multi-modal deep learning approach
Scientific reports, Vol.9(1), pp.1952-1952
02/13/2019
DOI: 10.1038/s41598-018-37769-z
PMCID: PMC6374429
PMID: 30760848
Abstract
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.
Details
- Title: Subtitle
- Predicting Alzheimer's disease progression using multi-modal deep learning approach
- Creators
- Garam Lee - Biomedical & Translational Informatics Institute, Geisinger, Danville, USAKwangsik Nho - Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USAByungkon Kang - Department of Software and Computer Engineering, Ajou University, Suwon, South KoreaKyung-Ah Sohn - Department of Software and Computer Engineering, Ajou University, Suwon, South Korea. kasohn@ajou.ac.krDokyoon Kim - The Huck Institute of the Life Sciences, Pennsylvania State University, University Park, USA. dkim@geisinger.eduAlzheimer’s Disease Neuroimaging Initiative
- Contributors
- Laura L Boles-Ponto (Contributor) - University of Iowa, Radiology
- Resource Type
- Journal article
- Publication Details
- Scientific reports, Vol.9(1), pp.1952-1952
- DOI
- 10.1038/s41598-018-37769-z
- PMID
- 30760848
- PMCID
- PMC6374429
- NLM abbreviation
- Sci Rep
- ISSN
- 2045-2322
- eISSN
- 2045-2322
- Publisher
- England
- Grant note
- CIHR R01 LM012535 / NLM NIH HHS UL1 TR002369 / NCATS NIH HHS U24 AG021886 / NIA NIH HHS P30 AG010129 / NIA NIH HHS R03 AG054936 / NIA NIH HHS U01 AG024904 / NIA NIH HHS
- Language
- English
- Date published
- 02/13/2019
- Academic Unit
- Radiology; Pharmaceutical Sciences and Experimental Therapeutics
- Record Identifier
- 9984051783802771
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