aceClassification of tau status with machine learning models in amyloid-positive cohorts
Although tau positron emission tomography (PET) imaging is effective for staging tau pathology, it is limited clinically by cost and availability. Machine learning models based on magnetic resonance imaging (MRI)- and amyloid PET-derived features may serve as useful screening tools for tau pathology. Multiple machine learning models were developed to classify tau positivity in the Braak III/IV region using structural MRI, amyloid PET, and demographic features. Alzheimer's Disease Neuroimaging Initiative (ADNI) (…
Machine learning models using structural MRI, amyloid PET, and demographic features can classify tau positivity in the Braak III/IV region in amyloid-positive cohorts, achieving AUC 0.92 and 85% accuracy on external validation.
Evidence
- Peer-reviewedAlzheimer's & Dementia2026-08-01
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Truvace Impact Record TRV-2026-0610, v1: “Classification of tau status with machine learning models in amyloid-positive cohorts.” Truvace, 2026-08-01. /record/TRV-2026-0610 (accessed at citation time). sha256 d07c1adae65ff51a…
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