Screening for Alzheimer’s disease in the community using an AI-driven screening platform: design of the PREDICTOM study - 26/03/26

Doi : 10.1016/j.tjpad.2026.100545

Anna-Katharine Brem ^a,^b,^⁎,^{^{1
Shared first authors}} , Zunera Khan ^a,^{^{1
Shared first authors}}, Jonas Radermacher ^c,^{^{1
Shared first authors}}, Kostas Georgiadis ^d, Ioulietta Lazarou ^d, Margarita Grammatikopoulou ^d, Ellie Pickering ^e, Johanna Mitterreiter ^f, Jon Arild Aakre ^g,^h, Nicholas J. Ashton ^i,^j,^k, Miguel Baquero ^l, Maria Beser-Robles ^l, Claire Braboszcz ^m, Sigurd Brandt ⁿ, James Brown ^o, Federica Cacciamani ^p,^q,^r, Sarah Campill ^s, Christopher Collins ^o, Pushkar Deshpande ⁿ, Ana Diaz ^s, Stanley Durrleman ^p,^t, Sebastiaan Engelborghs ^u, Laura Ferré-González ^l, Giovani B. Frisoni ^v,^w, Martha Therese Gjestsen ^g,^h, Dianne Gove ^s, Lee Honigberg ^x, Bin Huang ^y, Anett Hudak ^z, Sandeep Kaushik ^aa, Tamas Letoha ^z, Gaby Marquardt ^f, Augusto J. Mendes ^v,^w, Matthias Müllenborn ^ab, Lucas Paletta ^ac, Nuno Pedrosa de Barros ^ad, Martin Pszeida ^ac, Audun Osland Vik-Mo ^g,^h, Hossein Rostamipour ^a, Robert Perneczky ^ae,^af,^ag,^ah,^ai, Boris-Stephan Rauchmann ^ae,^aj, Silvia Russegger ^ac, Timo Schirmer ^aa, Amied Shadmaan ^ak, Ana Beatriz Solana ^aa,^al, Aureli Soria-Frisch ^m, Paulina Tegethoff ^ae, Annemie Ribbens ^ad, Sara De Witte ^u, Mark van der Giezen ^am,^an,^ao, Spiros Nikolopoulos ^d, Anne Corbett ^e, Holger Fröhlich ^c,^ap,^aq, Dag Aarsland ^a,^g,^⁎

the PREDICTOM Consortium

^a Centre for Healthy Brain Ageing, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom

^b University Hospital of Old Age Psychiatry, University of Bern, Bern, Switzerland

^c Department of Biomedical AI & Data Science, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt, Augustin, Germany

^d Centre for Research and Technology Hellas, Information Technologies Institute (CERTH-ITI), Thessaloniki, Greece

^e Dept of Health and Community Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK

^f Center for Innovation in Diagnostics, Siemens Healthineers AG, Forchheim, Germany

^g Centre for age-related Medicine – SESAM, Stavanger University Hospital, Stavanger, Norway

^h Department of Clinical Medicine, University of Bergen, Bergen, Norway

ⁱ Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Molndal, Sweden

^j Banner Alzheimer's Institute and University of Arizona, Phoenix, AZ, USA

^k Banner Sun Health Research Institute, Sun City, AZ, USA

^l Instituto de Investigación Sanitaria La Fe, Valencia, Spain

^m Starlab Barcelona, Barcelona, Spain

ⁿ GN Hearing, Ballerup, Denmark

^o Muhdo Health Ltd, Ipswich, United Kingdom

^p Qairnel, Paris, France

^q Ecole Normale Supérieure (ENS), NPI Lab, Department of Cognitive Studies, PSL University, Paris, France

^r Institut Mondor de Recherche Biomédicale (IMRB), Henri Mondor Hospital, AP-HP, Créteil, France

^s Alzheimer Europe a.s.b.l., Sennigerberg, Luxembourg

^t ARAMISLab, Paris Brain Institute – ICM, Sorbonne Université, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié Salpêtrière, Paris, France

^u Neuroprotection and Neuromodulation Research Group (NEUR), Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB) and Department of Neurology and Bru-BRAIN, Universitair Ziekenhuis Brussel, Brussels, Belgium

^v Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland

^w Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland

^x ALZpath, Carlsbad, CA, USA

^y BrainCheck Inc, Austin, USA

^z Pharmacoidea Ltd, Szeged, Hungary

^aa Ge HealthCare, Munich, Germany

^ab Novo Nordisk A/S, Søborg, Denmark

^ac Joanneum Research, Institute for Digital Technologies, Graz, Austria

^ad Icometrix, Leuven, Belgium

^ae Department of Psychiatry and Psychotherapy, LMU Hospital, LMU Munich, Munich, Germany

^af German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany

^ag Munich Cluster for Systems Neurology (SyNergy), Munich, Germany

^ah Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK

^ai Division of Neuroscience, University of Sheffield, Sheffield, UK

^aj Department of Neuroradiology, LMU Hospital, LMU Munich, Germany

^ak GE HealthCare, London, UK

^al Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry and Maudsley Hospital, King’s College London, London, United Kingdom

^am Research Department, Stavanger University Hospital, Stavanger, Norway

^an Department of Chemistry, Bioscience, and Environmental Engineering, University of Stavanger, Stavanger, Norway

^ao Natural Resources Institute, University of Greenwich, Chatham Maritime, Kent, United Kingdom

^ap Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany

^aq University of Bonn, University Hospital Bonn, Institute for Digital Medicine, Germany

^⁎ Corresponding authors.

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Abstract

Background

Recent developments in physiological, imaging and digital biomarkers combined with the approval of new disease-modifying drugs against Alzheimer’s disease (AD) and diagnostic blood tests provide an opportunity to shift the first diagnostic steps to the home-setting. While these novel biomarkers enable scalable screening and earlier detection and treatment of AD, they require an evaluation of their accuracy, feasibility, and safety in primary care and the community setting.

Objectives

The aim of PREDICTOM is to develop and test the accuracy of an artificial intelligence (AI) driven screening platform for the risk assessment and early detection of AD to extend the clinical pathway to home-based screening using established and novel biomarkers.

Design/setting

PREDICTOM is a European (Norway, UK, Belgium, France, Switzerland, Germany, Spain) observational, prospective cohort study using a cloud-based platform that stores a digitalised journey for each participant and provides a collection of artificial-intelligence (AI) algorithms and tools for risk assessment and early diagnosis and prognosis.

Participants

Cohort 1 consists of 4000 adults aged 50 years or older at risk of developing AD. Cohort 2 consists of 615 participants selected from Cohort 1 based on estimates indicating high ( N = 415) or low ( N = 200) risk of AD. Data from existing cohorts will guide the analytic strategy of the study.

Measurements

Cohort 1 will undergo home-based assessments (Level 1), Cohort 2 will undergo in-clinic assessments (Levels 2 and 3). Level 1 includes at-home screening, collecting digital and physiological data (questionnaires, cognition, hearing, eye-tracking) and biofluids (capillary blood via finger-stick and saliva) for biomarker analysis. Level 2 comprises a more complex biomarker collection, most of which can be completed in primary care, including EEG, MRI, venous blood, microbiome from stool, cognition, hearing, and eye-tracking. Level 3 includes a diagnostic evaluation to confirm or rule out AD pathology using established biomarkers (cerebrospinal fluid, or amyloid PET).

Conclusions

PREDICTOM will develop AI-driven algorithms for the early detection of AD using biomarkers that can be collected at home or in the community care setting, and evaluate their integration into a well-defined and comprehensive clinical pathway.

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Keywords : Alzheimer’s disease, Artificial intelligence, Early detection, Biomarker

Plan

Introduction

Methods

Overview and setting

Source of recruitment

Screening, eligibility, and consenting

Facilitate change in clinical practice

Public involvement (PI)

Level 1: home-based assessment of digital and physiological data

Self- and informant-rated questionnaires

Collection of cognitive data

Collection of physiological data

Collection of biofluids

Level 2: in-clinic procedures

Collection of clinical and cognitive data

Collection of physiological data

Collection of biofluids

Level 3: diagnostic validation

Data infrastructure and predictive modeling

Sample size estimate, statistical analysis and AI model development

AI-based risk stratification to guide the selection for level 2 testing

Statistical analysis

Development of novel diagnostic machine learning models

Feasibility of home-based testing

Recruitment status at the end of november 2025

Discussion

Strengths

Limitations and challenges

Availability of data and material

Conflict of interest disclosures

Declaration of generative AI and AI-assisted technologies in the writing process

Funding

CRediT authorship contribution statement

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Vol 13 - N° 5

Article 100545- mai 2026 Retour au numéro

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Screening for Alzheimer’s disease in the community using an AI-driven screening platform: design of the PREDICTOM study - 26/03/26

Abstract

Background

Objectives

Design/setting

Participants

Measurements

Conclusions

Plan

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