Multimodal deep learning models for early detection of Alzheimer’s disease stage
Open accessAbstract
Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single data modality to make predictions such as AD stages. The fusion of multiple data modalities can provide a holistic view of AD staging analysis. Thus, we use deep learning (DL) to integrally analyze imaging (magnetic resonance imaging (MRI)), genetic (single nucleotide polymorphisms (SNPs)), and clinical test data to classify patients into AD, MCI, and controls (CN). We use stacked denoising auto-encoders to extract features from clinical and genetic data, and use 3D-convolutional neural networks (CNNs) for imaging data. We also develop a novel data interpretation method to identify top-performing features learned by the deep-models with clustering and perturbation analysis. Using Alzheimer's disease neuroimaging initiative (ADNI) dataset, we demonstrate that deep models outperform shallow models, including support vector machines, decision trees, random forests, and k-nearest neighbors. In addition, we demonstrate that integrating multi-modality data outperforms single modality models in terms of accuracy, precision, recall, and meanF1 scores. Our models have identified hippocampus, amygdala brain areas, and the Rey Auditory Verbal Learning Test (RAVLT) as top distinguished features, which are consistent with the known AD literature.
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Various combinations of imaging, clinical, and genomic modalities can be fused for tasks such as AD prediction, AD stage detection, and liver transplantation risk assessment.
“For instance, MRI and clinical data can be fused for AD prediction [72] or MRI, clinical and genomic modalities can be fused for AD stage detection [69]. Also, latent representations of multiple imaging modalities and clinical data can be fused for risk assessment of liver transplantation for hepatocellular cancer [71].”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [69]
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A Robust Distributed Deep Learning Approach to Detect Alzheimer’s Disease from MRI Images
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Ensemble deep learning for Alzheimer’s disease characterization and estimation
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A Novel AI-Based System for Detection and Severity Prediction of Dementia Using MRI
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Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification
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EAD-DNN: Early Alzheimer's disease prediction using deep neural networks
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Deep learning-based approach for multi-stage diagnosis of Alzheimer’s disease
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Predicting early Alzheimer’s with blood biomarkers and clinical features
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Multimodal Neuroimaging Based Alzheimer's Disease Diagnosis Using Evolutionary RVFL Classifier
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Multimodality Fusion Aspects of Medical Diagnosis: A Comprehensive Review
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Automatic Early Diagnosis of Alzheimer’s Disease Using 3D Deep Ensemble Approach
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- Authors
- Venugopalan, J., Tong, L., Hassanzadeh, H. R., & Wang, M. D.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Wang, May D.
- Venue
- Scientific Reports
- Published
- 2021-02-05
- Volume
- 11
- Issue
- 1
- Pages
- 3254-3254
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7864942
- OpenAlex
- W3127597934
- Cited by
- 732
- References
- 57
- Citations by year
- 2026: 113 · 2025: 181 · 2024: 162 · 2023: 141 · 2022: 108 · 2021: 27
- FWCI
- 78.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (UL1TR000454)
- Microsoft Research
- Hewlett Packard | HP Labs
- U.S. Department of Health & Human Services | NIH | Center for Information Technology (UL1TR000454)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Alzheimer's disease, mild cognitive impairment, disease staging, multimodal fusion, magnetic resonance imaging, single nucleotide polymorphisms, genetic data, stacked denoising autoencoder, 3D convolutional neural networks, feature interpretation, hippocampus, amygdala, Rey Auditory Verbal Learning Test, ADNI
- MeSH terms
- Deep Learning, Alzheimer Disease, Diagnosis, Computer-Assisted, Humans, Early Diagnosis, Data Mining
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