Predicting Alzheimer’s disease progression using multi-modal deep learning approach
| Autor: | Lee, Garam, Nho, Kwangsik, Kang, Byungkon, Sohn, Kyung Ah, Kim, Dokyoon, Weiner, Michael W., Aisen, Paul, Petersen, Ronald, Jack, Clifford R., Jagust, William, Trojanowki, John Q., Toga, Arthur W., Beckett, Laurel, Green, Robert C., Saykin, Andrew J., Morris, John, Shaw, Leslie M., Khachaturian, Zaven, Sorensen, Greg, Carrillo, Maria, Kuller, Lew, Raichle, Marc, Paul, Steven, Davies, Peter, Fillit, Howard, Hefti, Franz, Holtzman, Davie, Mesulam, M. Marcel, Potter, William, Snyder, Peter, Montine, Tom, Thomas, Ronald G. |
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| Rok vydání: | 2019 |
| Předmět: |
Male
0301 basic medicine Diagnostic guidelines National Institute Cross-sectional study lcsh:Medicine Disease Recommendations Association workgroups Cognition 0302 clinical medicine Medicine lcsh:Science Aged 80 and over Multidisciplinary Brain 3. Good health Phenotypes Area Under Curve Cohort Disease Progression Female Anatomy MRI medicine.medical_specialty Neuroimaging tau Proteins Article Cell and Developmental Biology 03 medical and health sciences Deep Learning Physical medicine and rehabilitation Alzheimer Disease Humans Cognitive Dysfunction Effects of sleep deprivation on cognitive performance Aged Amyloid beta-Peptides business.industry Deep learning lcsh:R Mild cognitive impairment Conversion Peptide Fragments MCI Clinical trial Cross-Sectional Studies 030104 developmental biology lcsh:Q Artificial intelligence business Biomarkers 030217 neurology & neurosurgery Forecasting |
| Zdroj: | Medical Biophysics Publications Scientific Reports, Vol 9, Iss 1, Pp 1-12 (2019) Anatomy and Cell Biology Publications Scientific Reports |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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