Predicting Alzheimer's disease progression using multi-modal deep learning approach.

Publication Type	Academic Article
Authors	Lee G, Nho K, Kang B, Sohn K, Kim D
Journal	Sci Rep
Volume	9
Issue	1
Pagination	1952
Date Published	02/13/2019
ISSN	2045-2322
Keywords	Alzheimer Disease, Cognitive Dysfunction, Forecasting
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.
DOI	10.1038/s41598-018-37769-z
PubMed ID	30760848
PubMed Central ID	PMC6374429