Screening for Alzheimer's disease in the community using an AI-driven screening platform: design of the PREDICTOM study.
Authors
Affiliations (32)
Affiliations (32)
- Centre for Healthy Brain Ageing, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; University Hospital of Old Age Psychiatry, University of Bern, Bern, Switzerland. Electronic address: [email protected].
- Centre for Healthy Brain Ageing, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
- Department of Biomedical AI & Data Science, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt, Augustin, Germany.
- Centre for Research and Technology Hellas, Information Technologies Institute (CERTH-ITI), Thessaloniki, Greece.
- Dept of Health and Community Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK.
- Center for Innovation in Diagnostics, Siemens Healthineers AG, Forchheim, Germany.
- Centre for age-related Medicine - SESAM, Stavanger University Hospital, Stavanger, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway.
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Molndal, Sweden; Banner Alzheimer's Institute and University of Arizona, Phoenix, AZ, USA; Banner Sun Health Research Institute, Sun City, AZ, USA.
- Instituto de Investigación Sanitaria La Fe, Valencia, Spain.
- Starlab Barcelona, Barcelona, Spain.
- GN Hearing, Ballerup, Denmark.
- Muhdo Health Ltd, Ipswich, United Kingdom.
- Qairnel, Paris, France; Ecole Normale Supérieure (ENS), NPI Lab, Department of Cognitive Studies, PSL University, Paris, France; Institut Mondor de Recherche Biomédicale (IMRB), Henri Mondor Hospital, AP-HP, Créteil, France.
- Alzheimer Europe a.s.b.l., Sennigerberg, Luxembourg.
- Qairnel, Paris, France; ARAMISLab, Paris Brain Institute - ICM, Sorbonne Université, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié Salpêtrière, Paris, France.
- 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.
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland; Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland.
- ALZpath, Carlsbad, CA, USA.
- BrainCheck Inc, Austin, USA.
- Pharmacoidea Ltd, Szeged, Hungary.
- Ge HealthCare, Munich, Germany.
- Novo Nordisk A/S, Søborg, Denmark.
- Joanneum Research, Institute for Digital Technologies, Graz, Austria.
- Icometrix, Leuven, Belgium.
- Department of Psychiatry and Psychotherapy, LMU Hospital, LMU Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK; Division of Neuroscience, University of Sheffield, Sheffield, UK.
- Department of Psychiatry and Psychotherapy, LMU Hospital, LMU Munich, Munich, Germany; Department of Neuroradiology, LMU Hospital, LMU Munich, Germany.
- GE HealthCare, London, UK.
- Ge HealthCare, Munich, Germany; Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry and Maudsley Hospital, King's College London, London, United Kingdom.
- Department of Psychiatry and Psychotherapy, LMU Hospital, LMU Munich, Munich, Germany.
- Research Department, Stavanger University Hospital, Stavanger, Norway; Department of Chemistry, Bioscience, and Environmental Engineering, University of Stavanger, Stavanger, Norway; Natural Resources Institute, University of Greenwich, Chatham Maritime, Kent, United Kingdom.
- Department of Biomedical AI & Data Science, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt, Augustin, Germany; Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany; University of Bonn, University Hospital Bonn, Institute for Digital Medicine, Germany.
- Centre for Healthy Brain Ageing, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Centre for age-related Medicine - SESAM, Stavanger University Hospital, Stavanger, Norway. Electronic address: [email protected].
Abstract
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. 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. 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. 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. 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). 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.