Evidence map›Paper›PMID 42533677›Full record

ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2026

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

Yun-Chi Lin, Sheng-Chieh Chiu, Rebecca Massey, Yu-Hua Dean Fang, Alzheimer's Disease Neuroimaging Initiative

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Classification of tau status with machine learning models in amyloid-positive cohorts.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yun-Chi LinDepartment of Biomedical Engineering, School of Engineering, University of Alabama at Birmingham, Birmingham, Alabama, USA.ORCID https://orcid.org/0009-0007-3739-4794
Sheng-Chieh ChiuDepartment of Biomedical Engineering, School of Engineering, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Rebecca MasseyDepartment of Radiology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Yu-Hua Dean FangAlzheimer's Disease Research Center, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.ORCID https://orcid.org/0000-0002-1039-7983
Alzheimer's Disease Neuroimaging Initiative

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Technology and Remote Assessment CoreU19AG063911 · NIA · MAYO CLINIC ROCHESTER · PI ADAM L. BOXER · 2019 to 2026
$120.9M
Early Onset AD Consortium - the LEAD Study (LEADS)U01AG057195 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI APOSTOLOVA, LIANA G, CARRILLO, MARIA C · 2018 to 2023
$70.3M
Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5M
The Clinical Significance of Incidental White Matter Lesions on MRI Amongst a Diverse Population with Cognitive Complaints (INDEED)U19NS120384 · NINDS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Kumar B. Rajan · 2020 to 2026
$61.9M
THERAPEUTIC EFFECTS OF INTRA-NASAL INSULIN DETEMIRP50AG005136 · NIA · UNIVERSITY OF WASHINGTON · PI GRABOWSKI, THOMAS J. · 1985 to 2019
$57.2M
The natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 2005 to 2025
$49.5M
SUPPLEMENT TO RUSH ALZHEIMERS DISEASE CENTER COREP30AG010161 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1991 to 2020
$49.1M
Satellite Diagnostic and Treatment Clinic CoreP50AG008702 · NIA · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI DE JAGER, PHILIP L · 1989 to 2019
$46.3M
National Alzheimer's Coordinating CenterU24AG072122 · NIA · UNIVERSITY OF WASHINGTON · PI STEPHENS, KARI A · 2021 to 2025
$45.8M
TREATMENT OF DEPRESSION IN ALZHEIMER'S DISEASEP50AG005133 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SWEET, ROBERT A · 1985 to 2019
$43.0M
Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
NCATS NIH HHS UL1 TR000448NCRR NIH HHS U24 RR021382NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS P20 AG068053NIA NIH HHS P20 AG068077NIA NIH HHS P20 AG068082NIA NIH HHS P30 AG010129NIA NIH HHS P30 AG010133NIA NIH HHS P30 AG010161NIA NIH HHS P30 AG013846NIA NIH HHS P30 AG013854NIA NIH HHS P30 AG019610NIA NIH HHS P30 AG035982NIA NIH HHS P30 AG053760NIA NIH HHS P30 AG062422NIA NIH HHS P30 AG062428NIA NIH HHS P30 AG062429NIA NIH HHS P30 AG066444NIA NIH HHS P30 AG066506NIA NIH HHS P30 AG066507NIA NIH HHS P30 AG066511NIA NIH HHS P30 AG066512NIA NIH HHS P30 AG066514NIA NIH HHS P30 AG066515NIA NIH HHS P30 AG066518NIA NIH HHS P30 AG072931NIA NIH HHS P30 AG072947NIA NIH HHS P30 AG072958NIA NIH HHS P30 AG072972NIA NIH HHS P30 AG072976NIA NIH HHS P30 AG072980NIA NIH HHS P30 AG086401NIA NIH HHS P50 AG005133NIA NIH HHS P50 AG005136NIA NIH HHS P50 AG005142NIA NIH HHS P50 AG008702NIA NIH HHS P50 AG016573NIA NIH HHS P50 AG016574NIA NIH HHS P50 AG033514NIA NIH HHS P50 AG047266NIA NIH HHS P50 AG047270NIA NIH HHS P50 AG047366NIA NIH HHS R01 AG019771NIA NIH HHS R01 AG021910NIA NIH HHS R01 AG043434NIA NIH HHS R01 AG045571NIA NIH HHS R01 AG052560NIA NIH HHS R01 AG053509NIA NIH HHS R01 AG053993NIA NIH HHS R01 AG054110NIA NIH HHS R01 AG055005NIA NIH HHS R01 AG056031NIA NIH HHS R01 AG056258NIA NIH HHS R01 AG056531NIA NIH HHS R01 AG058724NIA NIH HHS R01 AG061788NIA NIH HHS R01 AG062276NIA NIH HHS R01 AG067781NIA NIH HHS R01 AG068338NIA NIH HHS R01 AG069453NIA NIH HHS R01 AG073235NIA NIH HHS R01 AG077444NIA NIH HHS R35 AG072262NIA NIH HHS R56 AG045571NIA NIH HHS U01 AG024904NIA NIH HHS U01 AG057195NIA NIH HHS U19 AG063911NIA NIH HHS U19 AG073153NIA NIH HHS U24 AG067418NIA NIH HHS U24 AG072122NIBIB NIH HHS R01 EB009352NIDCD NIH HHS R01 DC008552NIH HHS P30AG086401NIH HHS S10 OD026738NIMH NIH HHS P50 MH071616NINDS NIH HHS P30 NS098577NINDS NIH HHS R01 NS075075NINDS NIH HHS U19 NS120384RRD VA I01 RX001534RRD VA I21 RX001381
6 · The paper itself

Abstract

introductionAlthough 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.

methodsMultiple 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) (n = 410) data were used for model training. Open Access Series of Imaging Studies (OASIS-3; n = 143) and the Standardized Centralized Alzheimer's Disease Neuroimaging (SCAN; n = 154) data were used for external validation.

resultsLogistic regression achieved the best performance with areas under the curve (AUCs) of 0.92 for both internal and external validation. Combined external validation yielded accuracy/sensitivity/specificity of 85%/83%/85%. Subjects with mild cognitive impairment and predicted tau positivity progressed to AD at a significantly faster pace (p < 10 DISCUSSION: Our model demonstrates the feasibility of classifying tau burden in amyloid-positive cohorts with MRI- and amyloid PET-derived features and may serve as a surrogate biomarker.

Indexed as

Alzheimer DiseaseMachine Learningtau ProteinsAgedAged, 80 and overBrainClassification AlgorithmsCognitive DysfunctionFemaleHumansMagnetic Resonance ImagingNeuroimagingPositron-Emission TomographyPredictive Learning Modelstau ProteinsAlzheimer's diseaseamyloid PETanti‐amyloid therapybiomarkersclinical decision‐makingdisease modificationdrug efficacymachine learning modelsMRIneuroimagingpersonalized medicinetau PET

Identifiers

PMID42533677
PMCPMC13425606

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