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

@article{brem2026screening,
  title = {{Screening for Alzheimer’s disease in the community using an AI-driven screening platform: design of the PREDICTOM study}},
  author = {Anna-Katharine Brem and Zunera Khan and Jonas Radermacher and Kostas Georgiadis and Ioulietta Lazarou and Margarita Grammatikopoulou and Ellie Pickering and Johanna Mitterreiter and Jon Arild Aakre and Nicholas Ashton and Miguel Baquero and Maria Beser-Robles and Claire Braboszcz and Sigurd Brandt and James Brown and Federica Cacciamani and Sarah Campill and Christopher Collins and Pushkar Deshpande and Ana Diaz and Stanley Durrleman and Sebastiaan Engelborghs and Laura Ferré-González and Giovani Frisoni and Martha Therese Gjestsen and Dianne Gove and Lee Honigberg and Bin Huang and Anett Hudak and Sandeep Kaushik and Tamas Letoha and Gaby Marquardt and Augusto Mendes and Matthias Müllenborn and Lucas Paletta and Nuno Pedrosa de Barros and Martin Pszeida and Audun Osland Vik-Mo and Hossein Rostamipour and Robert Perneczky and Boris-Stephan Rauchmann and Silvia Russegger and Timo Schirmer and Amied Shadmaan and Ana Beatriz Solana and Aureli Soria-Frisch and Paulina Tegethoff and Annemie Ribbens and Sara de Witte and Mark van der Giezen and Spiros Nikolopoulos and Anne Corbett and Holger Fröhlich and Dag Aarsland},
  journal = {The Journal of prevention of Alzheimer's disease},
  year = {2026},
  month = oct,
  doi = {10.1016/j.tjpad.2026.100545},
  url = {https://hal.science/hal-05786441v1},
  note = {CC BY},
}

TY  - JOUR
TI  - Screening for Alzheimer’s disease in the community using an AI-driven screening platform: design of the PREDICTOM study
AU  - Anna-Katharine Brem
AU  - Zunera Khan
AU  - Jonas Radermacher
AU  - Kostas Georgiadis
AU  - Ioulietta Lazarou
AU  - Margarita Grammatikopoulou
AU  - Ellie Pickering
AU  - Johanna Mitterreiter
AU  - Jon Arild Aakre
AU  - Nicholas Ashton
AU  - Miguel Baquero
AU  - Maria Beser-Robles
AU  - Claire Braboszcz
AU  - Sigurd Brandt
AU  - James Brown
AU  - Federica Cacciamani
AU  - Sarah Campill
AU  - Christopher Collins
AU  - Pushkar Deshpande
AU  - Ana Diaz
AU  - Stanley Durrleman
AU  - Sebastiaan Engelborghs
AU  - Laura Ferré-González
AU  - Giovani Frisoni
AU  - Martha Therese Gjestsen
AU  - Dianne Gove
AU  - Lee Honigberg
AU  - Bin Huang
AU  - Anett Hudak
AU  - Sandeep Kaushik
AU  - Tamas Letoha
AU  - Gaby Marquardt
AU  - Augusto Mendes
AU  - Matthias Müllenborn
AU  - Lucas Paletta
AU  - Nuno Pedrosa de Barros
AU  - Martin Pszeida
AU  - Audun Osland Vik-Mo
AU  - Hossein Rostamipour
AU  - Robert Perneczky
AU  - Boris-Stephan Rauchmann
AU  - Silvia Russegger
AU  - Timo Schirmer
AU  - Amied Shadmaan
AU  - Ana Beatriz Solana
AU  - Aureli Soria-Frisch
AU  - Paulina Tegethoff
AU  - Annemie Ribbens
AU  - Sara de Witte
AU  - Mark van der Giezen
AU  - Spiros Nikolopoulos
AU  - Anne Corbett
AU  - Holger Fröhlich
AU  - Dag Aarsland
PY  - 2026
DA  - 2026/10/08
JO  - The Journal of prevention of Alzheimer's disease
DO  - 10.1016/j.tjpad.2026.100545
UR  - https://hal.science/hal-05786441v1
AB  - 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.
SN  - 2274-5807
SN  - 2426-0266
ER  -