Portal de investigación
Screening for Alzheimer’s disease in the community using an AI-driven screening platform: design of the PREDICTOM study
Fecha de publicación:
Autores de IIS La Fe
Participantes ajenos a IIS La Fe
- Brem A.-K.
- Khan Z.
- Radermacher J.
- Georgiadis K.
- Lazarou I.
- Grammatikopoulou M.
- Pickering E.
- Mitterreiter J.
- Aakre J.A.
- Ashton N.J.
- Braboszcz C.
- Brandt S.
- Brown J.
- Cacciamani F.
- Campill S.
- Collins C.
- Deshpande P.
- Diaz A.
- Durrleman S.
- Engelborghs S.
- Frisoni G.B.
- Gjestsen M.T.
- Gove D.
- Honigberg L.
- Huang B.
- Hudak A.
- Kaushik S.
- Letoha T.
- Marquardt G.
- Mendes A.J.
- Müllenborn M.
- Paletta L.
- de Barros N.P.
- Pszeida M.
- Vik-Mo A.O.
- Rostamipour H.
- Perneczky R.
- Rauchmann B.-S.
- Russegger S.
- Schirmer T.
- Shadmaan A.
- Solana A.B.
- Soria-Frisch A.
- Tegethoff P.
- Ribbens A.
- De Witte S.
- van der Giezen M.
- Nikolopoulos S.
- Corbett A.
- Fröhlich H.
- Aarsland D.
Grupos
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. © 2026 The Authors.
Datos de la publicación
- ISSN/ISSNe:
- 2274-5807, 2426-0266
- Tipo:
- Article
- Páginas:
- -
- PubMed:
- 41894949
- Enlace a otro recurso:
- www.scopus.com
- Factor de Impacto:
- 1,653 SCImago ℠
- Cuartil:
- Q1 SCImago ℠
JPAD-Journal of Prevention of Alzheimers Disease ELSEVIER
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- No hay documentos
Filiaciones
Keywords
- Aged; Alzheimer Disease; Artificial Intelligence; Biomarkers; Early Diagnosis; Europe; Female; Humans; Male; Mass Screening; Middle Aged; Prospective Studies; Risk Assessment; biological marker; florbetaben; florbetapir f 18; flutemetamol f 18; Pittsburgh compound B; biological marker; adult; aged; algorithm; Alzheimer disease; Article; artificial intelligence-assisted diagnosis; Belgium; biomedical technology assessment; blood analysis; capillary blood; cerebrospinal fluid; cognition; cognitive function test; cohort analysis; data collection method; diagnostic accuracy; diagnostic test accuracy study; early diagnosis; electroencephalography; eye tracking; feces analysis; female; France; Germany; hearing; human; machine learning algorithm; major clinical study; male; microbiome; middle aged; multicenter study; Norway; nuclear magnetic resonance imaging; observational study; open source technology; patient identification; positron emission tomography; predictive value; primary medical c
Cita
Brem A-K,Khan Z,Radermacher J,Georgiadis K,Lazarou I,Grammatikopoulou M,Pickering E,Mitterreiter J,Aakre JA,Ashton NJ,BAQUERO M,BESER M,Braboszcz C,Brandt S,Brown J,Cacciamani F,Campill S,Collins C,Deshpande P,Diaz A,Durrleman S,Engelborghs S,FERRÉ L,Frisoni GB,Gjestsen MT,Gove D,Honigberg L,Huang B,Hudak A,Kaushik S,Letoha T,Marquardt G,Mendes AJ,Müllenborn M,Paletta L,de Barros NP,Pszeida M,Vik AO,Rostamipour H,Perneczky R,Rauchmann B-S,Russegger S,Schirmer T,Shadmaan A,Solana AB,Soria A,Tegethoff P,Ribbens A,De Witte S,van der Giezen M,Nikolopoulos S,Corbett A,Fröhlich H,Aarsland D. Screening for Alzheimer’s disease in the community using an AI-driven screening platform: design of the PREDICTOM study. JPAD-J. Prev. Alzheimers Dis. 2026. 13. (5):100545. IF:6,700. (1).