ArticleScientific reports2021
Multimodal deep learning models for early detection of Alzheimer's disease stage.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 191 papers, 6 of them syntheses that pooled it.
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Who cites it
191 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Biomarkers for Alzheimer's disease across diverse biological domains: an umbrella review and evidence map.Journal of advanced research · 2026Pooled it
- Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Multimodal Deep Learning Approaches for Early Detection of Alzheimer's Disease: A Comprehensive Systematic Review of Image Processing Techniques.Current Alzheimer research · 2025Pooled it
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- Interpretable machine learning for dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Pooled it
- Biomarker-Conditioned Vision Transformers with Deformable Biomarker Attention for Alzheimer's Disease Classification.Journal of imaging informatics in medicine · 2026Article
- Multi-dimensional attention framework for personalised Alzheimer's disease progression prediction across sporadic and genetic risk cohorts.Research square · 2026Article
- Automated Triage for New Keratoconus Referrals Using Multimodal Deep Learning.Ophthalmology science · 2026Article
- Applying ensemble machine learning techniques to MRI scans to predict Alzheimer's disease.Scientific reports · 2026Article
- Clinical pathways matter for multimodal deep learning in early Alzheimer's disease detection.Scientific reports · 2026Article
- Real-World Multimodal Machine Learning for Risk Enrichment Across the Alzheimer's Disease Spectrum.Journal of clinical medicine · 2026Article
- AutoML-Multiverse: An Instability-Aware Framework for Quantifying Analytic Variability in Alzheimer's Disease Machine-Learning Studies.medRxiv : the preprint server for health sciences · 2026Article
- Multi-Modal Fusion with Supervised Contrastive Learning Model for Early Alzheimer's Disease Diagnosis and Multi-Modal Biomarker Identification.Interdisciplinary sciences, computational life sciences · 2026Article
- MRI neuroimaging-based Alzheimer's disease stage classification using deep neural network with convolutional block attention module and GAN-style noise injection.Scientific reports · 2026Article
- Integration of AI diagnostic tools into clinical practice for Alzheimer's disease: barriers and solutions.Annals of medicine and surgery (2012) · 2026Review
- CLinNET: An Interpretable and Uncertainty-Aware Deep Learning Framework for Multi-Modal Clinical Genomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Functional and Clinical: An Explainable Deep Learning Model for Multimodal Alzheimer's Disease Classification.Brain and behavior · 2026Article
- Integrative multimodal hybrid data fusion for mortality prediction.Scientific reports · 2026Article
- A Dual Stream Deep Learning Framework for Alzheimer's Disease Detection Using MRI Sonification.Journal of imaging · 2026Article
131 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.