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

By Garam Lee, Kwangsik Nho, Byungkon Kang, Kyung-ah Sohn, Dokyoon Kim, for the Alzheimer’s Disease Neuroimaging Initiative. Scientific Reports, 2019

385 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Epidemiology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/3570dc2c88cad8889fdd22059e8c0bf526dec64a

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In Artificial Intelligence

1. Shaker El-Sappagh
2. José Maria Alonso
3. K. Kwak
4. S. M. Riazul Islam
5. Ahmad M Sultan

Citations in the corpus

• A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease (2021, background): “There is an emerging need for advanced AD detection and prediction models that can serve as a helping hand for medical practitioners to diagnose or detect the disease earlier and more accurately 5,19,20 .”