ArticleScientific reports2024
A multimodal machine learning model for predicting dementia conversion in Alzheimer's disease.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.Journal of medical Internet research · 2026Pooled it
- Machine learning applications in vascular neuroimaging for the diagnosis and prognosis of cognitive impairment and dementia: a systematic review and meta-analysis.Alzheimer's research & therapy · 2025Pooled it
- Neuroinflammatory and molecular pathways in Alzheimer's disease: mechanistic crosstalk and emerging therapeutic opportunities.Inflammopharmacology · 2026Review
- Article
- Navigating the Artificial Intelligence Revolution in Clinical Neurology: A New Multidisciplinary Task Force Within the European Academy of Neurology.European journal of neurology · 2026Article
- Applying ensemble machine learning techniques to MRI scans to predict Alzheimer's disease.Scientific reports · 2026Article
- [Progress on prognostic assessment methods for mild cognitive impairment].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2026Review
- Machine-learning prediction and risk stratification of 12-month cognitive decline in Alzheimer's disease using routine clinical and MRI data.Scientific reports · 2026Article
- Bidirectional prognostic predictions of conversion and reversion in amnestic mild cognitive impairment.GeroScience · 2026Article
- Artificial neural networks fighting real neural decline: a systematic review of AI in Alzheimer's research.Artificial intelligence review · 2026Article
- Abnormal coordination of executive control network and default mode network in cognitive impairment after deep nuclei intracerebral hemorrhage.Frontiers in neurology · 2026Article
- Classification and prediction of Alzheimer's disease stages and conversion from mild cognitive impairment based on multimodal data fusion.Frontiers in aging neuroscience · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- LNODE: Uncovering the Latent Dynamics ofMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Artificial intelligence and machine learning for precision prevention of cognitive decline: integrating multimodal biomarkers, lifestyle interventions, and natural medicines from prediction to clinical practice.Frontiers in aging neuroscience · 2026Review
- Early detection of Alzheimer's disease progression: comparative evaluation of deep learning models.Scientific reports · 2025Article
- Explainable hierarchical machine-learning approaches for multimodal prediction of conversion from mild cognitive impairment to Alzheimer's disease.Physical and engineering sciences in medicine · 2025Article
- Multi-Modal Deep Clustering Survival Machines for Alzheimer's Disease Subtype Discovery.... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision · 2025Article
- Brain Connectivity Estimation Network for the Identification of Dementia.Brain sciences · 2025Article
- The Role of Quantitative EEG in the Diagnosis of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Review
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Authors and funding
16 authors.
Funding
Abstract
Alzheimer's disease (AD) accounts for 60-70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10-15% of people annually convert to AD. We aimed to investigate the most robust model and modality combination by combining multi-modality image features based on demographic characteristics in six machine learning models. A total of 196 subjects were enrolled from four hospitals and the Alzheimer's Disease Neuroimaging Initiative dataset. During the four-year follow-up period, 47 (24%) patients progressed from MCI to AD. Volumes of the regions of interest, white matter hyperintensity, and regional Standardized Uptake Value Ratio (SUVR) were analyzed using T1, T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRIs, and amyloid PET (αPET), along with automatically provided hippocampal occupancy scores (HOC) and Fazekas scales. As a result of testing the robustness of the model, the GBM model was the most stable, and in modality combination, model performance was further improved in the absence of T2-FLAIR image features. Our study predicts the probability of AD conversion in MCI patients, which is expected to be useful information for clinician's early diagnosis and treatment plan design.
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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.