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@ARTICLE{Brem:286095,
      author       = {Brem, Anna-Katharine and Khan, Zunera and Radermacher,
                      Jonas and Georgiadis, Kostas and Lazarou, Ioulietta and
                      Grammatikopoulou, Margarita and Pickering, Ellie and
                      Mitterreiter, Johanna and Aakre, Jon Arild and Ashton,
                      Nicholas J. and Baquero, Miguel and Beser-Robles, Maria and
                      Braboszcz, Claire and Brandt, Sigurd and Brown, James and
                      Cacciamani, Federica and Campill, Sarah and Collins,
                      Christopher and Deshpande, Pushkar and Diaz, Ana and
                      Durrleman, Stanley and Engelborghs, Sebastiaan and
                      Ferré-González, Laura and Frisoni, Giovani B. and
                      Gjestsen, Martha Therese and Gove, Dianne and Honigberg, Lee
                      and Huang, Bin and Hudak, Anett and Kaushik, Sandeep and
                      Letoha, Tamas and Marquardt, Gaby and Mendes, Augusto J. and
                      Müllenborn, Matthias and Paletta, Lucas and de Barros, Nuno
                      Pedrosa and Pszeida, Martin and Vik-Mo, Audun Osland and
                      Rostamipour, Hossein and Perneczky, Robert and Rauchmann,
                      Boris Stephan and Russegger, Silvia and Schirmer, Timo and
                      Shadmaan, Amied and Solana, Ana Beatriz and Soria-Frisch,
                      Aureli and Tegethoff, Paulina and Ribbens, Annemie and De
                      Witte, Sara and van der Giezen, Mark and Nikolopoulos,
                      Spiros and Corbett, Anne and Fröhlich, Holger and Aarsland,
                      Dag},
      title        = {{S}creening for {A}lzheimer’s disease in the community
                      using an {AI}-driven screening platform: design of the
                      {PREDICTOM} study},
      journal      = {The journal of prevention of Alzheimer's disease},
      volume       = {13},
      number       = {5},
      issn         = {2274-5807},
      address      = {[Paris]},
      publisher    = {Elsevier Masson SAS},
      reportid     = {DZNE-2026-00391},
      pages        = {100545},
      year         = {2026},
      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.},
      keywords     = {Alzheimer’s disease (Other) / Artificial intelligence
                      (Other) / Biomarker (Other) / Early detection (Other)},
      cin          = {AG Dichgans},
      ddc          = {610},
      cid          = {I:(DE-2719)5000022},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.tjpad.2026.100545},
      url          = {https://pub.dzne.de/record/286095},
}