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@ARTICLE{Knig:257518,
      author       = {König, A. and Linz, N. and Baykara, E. and Tröger, J. and
                      Ritchie, C. and Saunders, S. and Teipel, S. and Köhler, S.
                      and Sánchez-Benavides, G. and Grau-Rivera, O. and Gispert,
                      J. D. and Palmqvist, S. and Tideman, P. and Hansson, O.},
      title        = {{S}creening over {S}peech in {U}nselected {P}opulations for
                      {C}linical {T}rials in {AD} ({PROSPECT}-{AD}): {S}tudy
                      {D}esign and {P}rotocol.},
      journal      = {The journal of prevention of Alzheimer's disease},
      volume       = {10},
      number       = {2},
      issn         = {2274-5807},
      address      = {Cham},
      publisher    = {Springer International Publishing},
      reportid     = {DZNE-2023-00429},
      pages        = {314-321},
      year         = {2023},
      abstract     = {Speech impairments are an early feature of Alzheimer's
                      disease (AD) and consequently, analysing speech performance
                      is a promising new digital biomarker for AD screening.
                      Future clinical AD trials on disease modifying drugs will
                      require a shift to very early identification of individuals
                      at risk of dementia. Hence, digital markers of language and
                      speech may offer a method for screening of at-risk
                      populations that are at the earliest stages of AD,
                      eventually in combination with advanced machine learning. To
                      this end, we developed a screening battery consisting of
                      speech-based neurocognitive tests. The automated test
                      performs a remote primary screening using a simple
                      telephone.PROSPECT-AD aims to validate speech biomarkers for
                      identification of individuals with early signs of AD and
                      monitor their longitudinal course through access to
                      well-phenotyped cohorts.PROSPECT-AD leverages ongoing
                      cohorts such as EPAD (UK), DESCRIBE and DELCODE (Germany),
                      and BioFINDER Primary Care (Sweden) and Beta-AARC (Spain) by
                      adding a collection of speech data over the telephone to
                      existing longitudinal follow-ups. Participants at risk of
                      dementia are recruited from existing parent cohorts across
                      Europe to form an AD 'probability-spectrum', i.e.,
                      individuals with a low risk to high risk of developing AD
                      dementia. The characterization of cognition, biomarker and
                      risk factor (genetic and environmental) status of each
                      research participants over time combined with audio
                      recordings of speech samples will provide a well-phenotyped
                      population for comparing novel speech markers with current
                      gold standard biomarkers and cognitive scores.N= 1000
                      participants aged 50 or older will be included in total,
                      with a clinical dementia rating scale (CDR) score of 0 or
                      0.5. The study protocol is planned to run according to sites
                      between 12 and 18 months.The speech protocol includes the
                      following neurocognitive tests which will be administered
                      remotely: Word List [Memory Function], Verbal Fluency
                      [Executive Functions] and spontaneous free speech
                      [Psychological and/ or behavioral symptoms]. Speech features
                      on the linguistic and paralinguistic level will be extracted
                      from the recordings and compared to data from CSF and blood
                      biomarkers, neuroimaging, neuropsychological evaluations,
                      genetic profiles, and family history. Primary candidate
                      marker from speech will be a combination of most significant
                      features in comparison to biomarkers as reference measure.
                      Machine learning and computational techniques will be
                      employed to identify the most significant speech biomarkers
                      that could represent an early indicator of AD pathology.
                      Furthermore, based on the analysis of speech performances,
                      models will be trained to predict cognitive decline and
                      disease progression across the AD continuum.The outcome of
                      PROSPECT-AD may support AD drug development research as well
                      as primary or tertiary prevention of dementia by providing a
                      validated tool using a remote approach for identifying
                      individuals at risk of dementia and monitoring individuals
                      over time, either in a screening context or in clinical
                      trials.},
      keywords     = {Humans / Alzheimer Disease: psychology / Biomarkers /
                      Cognitive Dysfunction: psychology / Memory / Speech /
                      Alzheimer’s disease (Other) / Dementia (Other) / cognitive
                      assessment (Other) / machine learning (Other) / phone-based
                      (Other) / screening (Other) / speech biomarker (Other) /
                      Biomarkers (NLM Chemicals)},
      cin          = {AG Teipel},
      ddc          = {610},
      cid          = {I:(DE-2719)1510100},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      experiment   = {EXP:(DE-2719)DELCODE-20140101 /
                      EXP:(DE-2719)Prospect-AD-20220101 /
                      EXP:(DE-2719)DESCRIBE-20150101},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36946458},
      pmc          = {pmc:PMC9851094},
      doi          = {10.14283/jpad.2023.11},
      url          = {https://pub.dzne.de/record/257518},
}