ArticleFrontiers in aging neuroscience2020
Predicting Alzheimer's Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data.
Article in Frontiers in aging neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it, 77 citations in OpenAlex.
- Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease.Alzheimer's research & therapy · 2023Pooled it
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
- Explainable machine learning for the prediction of Alzheimer's disease-related cognitive impairment: a consensus feature selection approach.BMC medical informatics and decision making · 2026Article
- Deep survival modeling to predict future cognitive impairment in unimpaired adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- Predicting categorical and continuous Alzheimer's disease outcomes from a single MRI scan.Nature aging · 2026Article
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- Detecting Mild Cognitive Impairment to Alzheimer's Disease Progression by fMRI Using Convolutional Neural Network and Long-short Term Memory.Basic and clinical neuroscience · 2025Article
- Artificial Intelligence in Alzheimer's Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval.Journal of clinical medicine · 2025Review
- Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- Flexible imputation toolkit for electronic health records.Scientific reports · 2025Article
- A multi-graph convolutional network method for Alzheimer's disease diagnosis based on multi-frequency EEG data with dual-mode connectivity.Frontiers in neuroscience · 2025Article
- Recent Advances in the Application of Artificial Intelligence in Alzheimer's Disease.Current Alzheimer research · 2025Review
- Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.Reviews on recent clinical trials · 2025Review
- The abnormal accumulation of pathological proteins and compensatory functional connectivity enhancement of insula subdivisions in mild cognitive impairment.Frontiers in aging neuroscience · 2025Article
- A hybrid transformer-based approach for early detection of Alzheimer's disease using MRI images.BioImpacts : BI · 2025Article
- Comparison of Ensemble Techniques for Early Prediction of Alzhiemer Disease.Research square · 2024Article
- Altered brain connectivity in mild cognitive impairment is linked to elevated tau and phosphorylated tau, but not to GAP-43 and Amyloid-β measurements: a resting-state fMRI study.Molecular brain · 2024Article
- A multimodal machine learning model for predicting dementia conversion in Alzheimer's disease.Scientific reports · 2024Article
- Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network.Scientific reports · 2024Article
- Adversarial Learning for MRI Reconstruction and Classification of Cognitively Impaired Individuals.IEEE access : practical innovations, open solutions · 2024Article
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
Identifying patients with mild cognitive impairment (MCI) who are at high risk of progressing to Alzheimer's disease (AD) is crucial for early treatment of AD. However, it is difficult to predict the cognitive states of patients. This study developed an extreme learning machine (ELM)-based grading method to efficiently fuse multimodal data and predict MCI-to-AD conversion. First, features were extracted from magnetic resonance (MR) images, and useful features were selected using a feature selection method. Second, multiple modalities of MCI subjects, including MRI, positron emission tomography, cerebrospinal fluid biomarkers, and gene data, were individually graded using the ELM method. Finally, these grading scores calculated from different modalities were fed into a classifier to discriminate subjects with progressive MCI from those with stable MCI. The proposed approach has been validated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, and an accuracy of 84.7% was achieved for an AD prediction within 3 years. Experiments on predicting AD conversion from MCI within different periods showed similar results with the 3-year prediction. The experimental results demonstrate that the proposed approach benefits from the efficient fusion of four modalities, resulting in an accurate prediction of MCI-to-AD conversion.
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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.