± HEALTH In our modern society, digital devices, social media platforms, and artificial intelligence (AI) tools have b…+ CLIMATE The growing complexity of electric vehicle charging station (EVCS) operations—driven by grid constraints, ren…± HEALTH Objectives This study aim to develop, compare and internally validate machine-learning models for predicting…+ HEALTH Diabetes management faces a number of obstacles, such as fragmented healthcare data, privacy concerns, poor e…− SCIENCE Background Craniofacial soft tissue thickness (CFSTT) is a critical parameter in forensic facial reconstructi…+ HEALTH Background Technological innovation in total joint arthroplasty (TJA) has largely focused on intraoperative p…± HEALTH Infective endocarditis (IE) continues to be an often fatal condition despite improvements in cardiac surgical…± HEALTH Purpose of review Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is the leading cause of d…
TruaceTracing the truth around AIFriday, September 11, 2026
Subscribe
Health·G Space·Evidence-backed gain·Published 2026-08-01

Classification of tau status with machine learning models in amyloid-positive cohorts

Abstract: 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) (…

TRV-2026-0610Peer-reviewedPermanent record — cite & verify
Classification of tau status with machine learning models in amyloid-positive cohorts

"Lung- Miliary tuberculosis Case 202" by Pulmonary Pathology is licensed under CC BY-SA 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/2.0/.

The quick read

By August 2026, researchers had trained machine learning models on ADNI data to predict tau PET positivity from more accessible MRI and amyloid PET features, then tested them on OASIS-3 and SCAN cohorts. Logistic regression reached AUCs of 0.92 in both internal and external validation, with combined external accuracy of 85%.

The result matters because tau PET is effective but limited clinically by cost and availability, so an MRI-based surrogate could expand screening in amyloid-positive populations. It remains uncertain how the model would perform outside research cohorts, across diverse clinical settings, and whether predicted tau status would change care decisions or outcomes.

Main points
  • Model trained on ADNI n=410 and externally validated on OASIS-3 n=143 and SCAN n=154 using MRI, amyloid PET, and demographic features.
  • Logistic regression was best performing model with AUC 0.92 internally and externally and 85%/83%/85% accuracy/sensitivity/specificity combined.
  • Subjects with mild cognitive impairment and predicted tau positivity progressed to AD at significantly faster pace with p < 10-6.
Gain

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.

The rundown

Researchers developed multiple machine learning models to classify tau positivity in the Braak III/IV region using structural MRI, amyloid PET, and demographic features, training on Alzheimer's Disease Neuroimaging Initiative data with n=410.

External validation used Open Access Series of Imaging Studies n=143 and Standardized Centralized Alzheimer's Disease Neuroimaging n=154, where logistic regression outperformed other models and predicted tau positivity was associated with faster progression from mild cognitive impairment to AD.

Sources

Reader signal

How should this claim be treated?

The debate