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@ARTICLE{Alexopoulou:281859,
      author       = {Alexopoulou, Zampeta-Sofia and Köhler, Stefanie and
                      Mallick, Elisa and Tröger, Johannes and Linz, Nicklas and
                      Spruth, Eike and Fliessbach, Klaus and Bartels, Claudia and
                      Rostamzadeh, Ayda and Glanz, Wenzel and Incesoy, Enise I and
                      Butryn, Michaela and Kilimann, Ingo and Sodenkamp, Sebastian
                      and Munk, Matthias Hj and Osterrath, Antje and Esser, Anna
                      and Roeske, Sandra and Frommann, Ingo and Stark, Melina and
                      Kleineidam, Luca and Spottke, Annika and Priller, Josef and
                      Schneider, Anja and Wiltfang, Jens and Jessen, Frank and
                      Düzel, Emrah and Falkenburger, Bjoern and Wagner, Michael
                      and Laske, Christoph and Manera, Valeria and Teipel, Stefan
                      and König, Alexandra},
      title        = {{S}peech-based digital cognitive assessments for detection
                      of mild cognitive impairment: {V}alidation against
                      paper-based neurocognitive assessment scores.},
      journal      = {Journal of Alzheimer's disease},
      volume       = {108},
      number       = {$1_suppl$},
      issn         = {1387-2877},
      address      = {Amsterdam},
      publisher    = {IOS Press},
      reportid     = {DZNE-2025-01232},
      pages        = {S118 - S131},
      year         = {2025},
      abstract     = {BackgroundCognitive decline in Alzheimer's disease (AD)
                      often includes speech impairments, where subtle changes may
                      precede clinical dementia onset. As clinical trials focus on
                      early identification of patients for disease-modifying
                      treatments, digital speech-based assessments for scalable
                      screening have become crucial.ObjectiveThis study aimed to
                      validate a remote, speech-based digital cognitive assessment
                      for mild cognitive impairment (MCI) detection through the
                      comparison with gold-standard paper-based neurocognitive
                      assessments.MethodsWithin the PROSPECT-AD project, speech
                      and clinical data were obtained from the German DELCODE and
                      DESCRIBE cohorts, including 21 healthy controls (HC), 110
                      participants with subjective cognitive decline (SCD), and 59
                      with MCI. Spearman rank and partial correlations were
                      computed between speech-based scores and clinical measures.
                      Kruskal-Wallis tests assessed group differences. We trained
                      machine learning models to classify diagnostic groups
                      comparing classification accuracies between gold-standard
                      assessment scores and a speech-based digital cognitive
                      assessment composite score (SB-C).ResultsGlobal cognition,
                      as measured by SB-C, significantly differed between
                      diagnostic groups (H(2) = 30.93, p < 0.001). Speech-based
                      scores were significantly correlated with global anchor
                      scores (MMSE, CDR, PACC5). Speech-based composites for
                      memory, executive function and processing speed were also
                      correlated with respective domain-specific paper-based
                      assessments. In logistic regression classification, the
                      model combining SB-C and neuropsychological tests at
                      baseline achieved a high discriminatory power in
                      differentiating HC/SCD from MCI patients (Area Under the
                      Curve = 0.86).ConclusionsOur findings support speech-based
                      cognitive assessments as a promising avenue towards remote
                      MCI screening, with implications for scalable screening in
                      clinical trials and healthcare.},
      keywords     = {Humans / Cognitive Dysfunction: diagnosis / Cognitive
                      Dysfunction: psychology / Male / Female / Aged /
                      Neuropsychological Tests / Speech: physiology / Aged, 80 and
                      over / Middle Aged / Cohort Studies / Alzheimer's disease
                      (Other) / dementia (Other) / digital cognitive assessment
                      (Other) / mild cognitive impairment (Other) / speech
                      analysis (Other) / speech-based digital cognitive assessment
                      (Other) / speech-based markers (Other)},
      cin          = {AG Teipel / AG Priller / Patient Studies (Bonn) / AG Düzel
                      / ICRU / AG Gasser / AG Falkenburger / AG Spottke / AG
                      Wagner / Clinical Research Platform (CRP) / AG Schneider /
                      AG Wiltfang / AG Jessen},
      ddc          = {610},
      cid          = {I:(DE-2719)1510100 / I:(DE-2719)5000007 /
                      I:(DE-2719)1011101 / I:(DE-2719)5000006 / I:(DE-2719)1240005
                      / I:(DE-2719)1210000 / I:(DE-2719)1710012 /
                      I:(DE-2719)1011103 / I:(DE-2719)1011201 / I:(DE-2719)1011401
                      / I:(DE-2719)1011305 / I:(DE-2719)1410006 /
                      I:(DE-2719)1011102},
      pnm          = {353 - Clinical and Health Care Research (POF4-353) / 899 -
                      ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-353 / G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40415342},
      pmc          = {pmc:PMC12583645},
      doi          = {10.1177/13872877251343296},
      url          = {https://pub.dzne.de/record/281859},
}