Screening for Alzheimer’s disease in the community using an AI-driven screening platform: design of the PREDICTOM study - 17/03/26

Doi : 10.1016/j.tjpad.2026.100545

Anna-Katharine Brem ^1,^2,^{⁎
Shared first authors}, ^⁎ , Zunera Khan ^1,^{⁎
Shared first authors}, Jonas Radermacher ^3,^{⁎
Shared first authors}, Kostas Georgiadis ⁴, Ioulietta Lazarou ⁴, Margarita Grammatikopoulou ⁴, Ellie Pickering ⁵, Johanna Mitterreiter ⁶, Jon Arild Aakre ^7,⁸, Nicholas J. Ashton ^9,^10,¹¹, Miguel Baquero ¹², Maria Beser-Robles ¹², Claire Braboszcz ¹³, Sigurd Brandt ¹⁴, James Brown ¹⁵, Federica Cacciamani ^16,^17,¹⁸, Sarah Campill ¹⁹, Christopher Collins ¹⁵, Pushkar Deshpande ¹⁴, Ana Diaz ¹⁹, Stanley Durrleman ^16,²⁰, Sebastiaan Engelborghs ²¹, Laura Ferré-González ¹², Giovani B. Frisoni ^22,²³, Martha Therese Gjestsen ^7,⁸, Dianne Gove ¹⁹, Lee Honigberg ²⁴, Bin Huang ²⁵, Anett Hudak ²⁶, Sandeep Kaushik ²⁷, Tamas Letoha ²⁶, Gaby Marquardt ⁶, Augusto J. Mendes ^22,²³, Matthias Müllenborn ²⁸, Lucas Paletta ²⁹, Nuno Pedrosa de Barros ³⁰, Martin Pszeida ²⁹, Audun Osland Vik-Mo ^7,⁸, Hossein Rostamipour ¹, Robert Perneczky ^31,^32,^33,^34,³⁵, Boris-Stephan Rauchmann ^31,³⁶, Silvia Russegger ²⁹, Timo Schirmer ²⁷, Amied Shadmaan ³⁷, Ana Beatriz Solana ^27,³⁸, Aureli Soria-Frisch ¹³, Paulina Tegethoff ³¹, Annemie Ribbens ³⁰, Sara De Witte ²¹, Mark van der Giezen ^39,^40,⁴¹, Spiros Nikolopoulos ⁴, Anne Corbett ⁵, Holger Fröhlich ^3,^42,⁴³, Dag Aarsland ^1,^7,^⁎

the PREDICTOM Consortium

¹ Centre for Healthy Brain Ageing, Department 1of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom

² University Hospital of Old Age Psychiatry, University of Bern, Bern, Switzerland

³ Department of Biomedical AI & Data Science, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt, Augustin, Germany

⁴ Centre for Research and Technology Hellas, Information Technologies Institute (CERTH-ITI), Thessaloniki, Greece

⁵ Dept of Health and Community Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK

⁶ Center for Innovation in Diagnostics, Siemens Healthineers AG, Forchheim, Germany

⁷ Centre for age-related Medicine – SESAM, Stavanger University Hospital, Stavanger, Norway

⁸ Department of Clinical Medicine, University of Bergen, Bergen, Norway

⁹ Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Molndal, Sweden

¹⁰ Banner Alzheimer's Institute and University of Arizona, Phoenix, AZ, USA

¹¹ Banner Sun Health Research Institute, Sun City, AZ, USA

¹² Instituto de Investigación Sanitaria La Fe, Valencia, Spain

¹³ Starlab Barcelona, Barcelona, Spain

¹⁴ GN Hearing, Ballerup, Denmark

¹⁵ Muhdo Health Ltd, Ipswich, United Kingdom

¹⁶ Qairnel, Paris, France

¹⁷ Ecole Normale Supérieure (ENS), NPI Lab, Department of Cognitive Studies, PSL University, Paris, France

¹⁸ Institut Mondor de Recherche Biomédicale (IMRB), Henri Mondor Hospital, AP-HP, Créteil, France

¹⁹ Alzheimer Europe a.s.b.l., Sennigerberg, Luxembourg

²⁰ ARAMISLab, Paris Brain Institute – ICM, Sorbonne Université, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié Salpêtrière, Paris, France

²¹ 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

²² Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland

²³ Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland

²⁴ ALZpath, Carlsbad, CA, USA

²⁵ BrainCheck Inc, Austin, USA

²⁶ Pharmacoidea Ltd, Szeged, Hungary

²⁷ Ge HealthCare, Munich, Germany

²⁸ Novo Nordisk A/S, Søborg, Denmark

²⁹ Joanneum Research, Institute for Digital Technologies, Graz, Austria

³⁰ Icometrix, Leuven, Belgium

³¹ Department of Psychiatry and Psychotherapy, LMU Hospital, LMU Munich, Munich, Germany

³² German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany

³³ Munich Cluster for Systems Neurology (SyNergy), Munich, Germany

³⁴ Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK

³⁵ Division of Neuroscience, University of Sheffield, Sheffield, UK

³⁶ Department of Neuroradiology, LMU Hospital, LMU Munich, Germany

³⁷ GE HealthCare, London, UK

³⁸ Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry and Maudsley Hospital, King’s College London, London, United Kingdom

³⁹ Research Department, Stavanger University Hospital, Stavanger, Norway

⁴⁰ Department of Chemistry, Bioscience, and Environmental Engineering, University of Stavanger, Stavanger, Norway

⁴¹ Natural Resources Institute, University of Greenwich, Chatham Maritime, Kent, United Kingdom

⁴² Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany

⁴³ University of Bonn, University Hospital Bonn, Institute for Digital Medicine, Germany

^# Corresponding authors: Phone: +41 (0)31 632 88 17, Phone: +44 (0)20 7836 5454

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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