ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2025
Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Blood-Based Tau as a Biomarker for Early Detection and Monitoring of Alzheimer's Disease: A Systematic Review and Meta-Analysis.International journal of molecular sciences · 2025Pooled it
- Plasma P-tau217 for detecting amyloid clearance after donanemab in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Trial
- Classification of tau status with machine learning models in amyloid-positive cohorts.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Tau-mediated Mechanisms in Alzheimer's Disease Pathogenesis.Molecular neurobiology · 2026Review
- Longitudinal plasma p-tau217 as a marker for tracking progression and predicting cognitive decline in Alzheimer's disease.Alzheimer's research & therapy · 2026Article
- Generating synthetic tau-PET scans in Alzheimer's disease from MRI, blood biomarkers and demographics with deep learning.medRxiv : the preprint server for health sciences · 2026Article
- Diagnostic and Prognostic Utility of Plasma p-tau217 for Alzheimer's Disease in Chinese Elderly: Insights From the SILCODE Study With a Derived Threshold.European journal of neurology · 2026Article
- Ratios of CSF proteins reflect cognitive function in ALS.Alzheimer's research & therapy · 2026Article
- Machine learning for the prediction of amyloid PET positivity using plasma biomarkers, cognition, APOE genotype, and structural imaging.Frontiers in neuroscience · 2026Article
- Blood biomarkers and artificial intelligence: toward precision medicine in Alzheimer's disease.Frontiers in aging neuroscience · 2026Review
- Cigarette smoke-induced lung-brain barrier dysfunction drives neurocognitive impairment via inflammatory spill-over.Journal of neuroinflammation · 2025Article
- Neuropathologically validated MRI to tau PET synthesis via Covariate-modulated attention networks.bioRxiv : the preprint server for biology · 2025Article
- Aligning personalized biomarker trajectories onto a common time axis: a connectome-based ODE model for Tau-Amyloid beta dynamics.Medical image analysis · 2025Article
- AI-driven fusion of multimodal data for Alzheimer's disease biomarker assessment.Nature communications · 2025Article
- Mapping Divergent Subfield-Specific Hippocampal Degeneration in Mild Cognitive Impairment Continuum: Volumetric, Cognitive, and Genetic Predictors of Accelerated Hippocampal Biological Aging.CNS neuroscience & therapeutics · 2025Article
- Personalised regional modelling predicts tau progression in the human brain.PLoS biology · 2025Article
- Back to the Future: Predicting Individual Tau Progression in Alzheimer's Disease.Research square · 2025Article
- Development of a Predictive Risk Model for Recurrence of Chronic Pulmonary Aspergillosis in Post-Tuberculosis Patients: A Retrospective Observational Study.International journal of general medicine · 2025Article
- Integration of Neuroimaging and Molecular Biomarkers in the Diagnosis of Alzheimer's Disease and Frontotemporal Dementia: The Promise of fMRI.Current Alzheimer research · 2025Review
Corrections and comments
- Erratum issued
- Update of
Authors and funding
20 authors.
Funding
Abstract
introductionTau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, but its routine clinical use is limited by cost and accessibility barriers.
methodsWe thoroughly investigated the ability of various machine learning models to predict clinically useful tau-PET composites (load and laterality index) from low-cost and non-invasive features, for example, clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI).
resultsModels including plasma biomarkers yielded the most accurate predictions of tau-PET burden (best model: R-squared = 0.66-0.69), with especially high contribution from plasma phosphorylated tau-217 (p-tau217). MRI variables were the best predictors of asymmetric tau load between the two hemispheres (best model: R-squared = 0.28-0.42). The models showed high generalizability to external test cohorts with data collected at multiple sites. Through a proof-of-concept two-step classification workflow, we also demonstrated possible model translations to a clinical setting. DISCUSSION: This study highlights the promising and limiting aspects of using machine learning to predict tau-PET from scalable cost-effective variables, with findings relevant for clinical settings and future research. HIGHLIGHTS: Accessible variables showed potential in estimating tau tangle load and distribution. Plasma phosphorylated tau-217 (p-tau217) and magnetic resonance imaging (MRI) were the best predictors of different tau-PET (positron emission tomography) composites. Machine learning models demonstrated high generalizability across AD cohorts.
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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.