Machine learning prediction of tau-PET in Alzheimer’s disease using plasma, MRI, and clinical data

Aug 3, 2026

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Tau positron emission tomography (PET) is a dependable imaging approach for quantifying regional tau pathology, yet routine clinical adoption is constrained by cost and limited availability. This study evaluates multiple machine learning models to predict clinically useful tau-PET composites, using low-cost and non-invasive inputs including clinical variables, plasma biomarkers, and structural MRI. Plasma biomarkers drive the most accurate tau-PET burden predictions, while MRI best explains hemispheric asymmetry.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

Methods

Results

Discussion

DOI: 10.1002/alz.14600 RESEARCH ARTICLE Machine learning prediction of tau-PET in Alzheimer’s disease using plasma, MRI, and clinical data Linda Karlsson1 Olof Strandberg1 Jacob Vogel2 Ida Arvidsson3 Kalle Åström3 Jakob Seidlitz4, 5, 6, 7 Richard A. I. Bethlehem8 Erik Stomrud1, 9 Rik Ossenkoppele1, 10 Kaj Blennow11, 13, 18, 19 Nicholas J. Ashton11, 12 Sebastian Palmqvist1, 9 Henrik Zetterberg11, 13, 14, 15, 16, 17 Ruben Smith1, 9 Shorena Janelidze1 Renaud LaJoie20 Gil D. Rabinovici20, 21 Alexa Pichet Binette1 Niklas Mattsson-Carlgren1, 9 Oskar Hansson1 1 Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden 2 Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden 3 Centre for Mathematical Sciences, Lund University, Lund, Sweden 4 Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, Pennsylvania, USA 5 Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA 6 Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA 7 Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA 8 University of Cambridge, Department of Psychology, Cambridge Biomedical Campus, Cambridge, UK 9 Memory Clinic, Skåne University Hospital, Malmö, Sweden 10Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands 11 Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden 12 Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Institute Clinical Neuroscience, King’s College London, London, UK 13 Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden 14 Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK 15 UK Dementia Research Institute at UCL, London, UK 16 Hong Kong Center for Neurodegenerative Diseases, 5/F, Building 5E, 5 Science Park East Avenue, Hong Kong Science Park, ClearWater Bay, Hong Kong, China 17Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA 18 Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France 19 Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, and Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC, Hefei, Anhui, P.R. China 20 Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, California, USA 21 Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA Correspondence Linda Karlsson and Oskar Hansson, Clinical Memory Research Unit, Department of Clinical Sciences Malmö, BMC C11, Lund University, Box 117, SE-22100 Lund, Sweden. Email: [linda.karlsson@med.lu.se and](linda.karlsson@med.lu.se and)[ ](linda.karlsson@med.lu.se and)[oskar.hansson@med.lu.se](oskar.hansson@med.lu.se) Abstract INTRODUCTION: Tau 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. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. © 2025 The Author(s). Alzheimer’s & Dementia published by Wiley Periodicals LLC on be

FAQs

Why is tau-PET not widely used in routine clinical care?

Tau-PET is reliable for measuring tau pathology, but its adoption is limited by cost and accessibility barriers.

What inputs were used to predict tau-PET composites?

The study uses low-cost, non-invasive features such as clinical variables, plasma biomarkers, and structural MRI.

Which data source contributed most to tau-PET burden prediction?

Models including plasma biomarkers produced the most accurate predictions, with especially strong contribution from plasma phosphorylated tau-217 (p-tau217).

Aug 3, 2026

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