% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Singh:282290,
      author       = {Singh, Devesh and Brima, Yusuf and Levin, Fedor and Becker,
                      Martin and Hiller, Bjarne and Hermann, Andreas and
                      Villar-Munoz, Irene and Beichert, Lukas and Bernhardt,
                      Alexander and Buerger, Katharina and Butryn, Michaela and
                      Dechent, Peter and Düzel, Emrah and Ewers, Michael and
                      Fliessbach, Klaus and Freiesleben, Silka D and Glanz, Wenzel
                      and Hetzer, Stefan and Janowitz, Daniel and Görß, Doreen
                      and Kilimann, Ingo and Kimmich, Okka and Laske, Christoph
                      and Levin, Johannes and Lohse, Andrea and Luesebrink, Falk
                      and Munk, Matthias and Perneczky, Robert and Peters, Oliver
                      and Preis, Lukas and Priller, Josef and Prudlo, Johannes and
                      Prychynenko, Diana and Rauchmann, Boris Stephan and
                      Rostamzadeh, Ayda and Roy-Kluth, Nina and Scheffler, Klaus
                      and Schneider, Anja and Droste Zu Senden, Louise and Schott,
                      Björn H and Spottke, Annika and Synofzik, Matthis and
                      Wiltfang, Jens and Jessen, Frank and Weber, Marc-André and
                      Teipel, Stefan J and Dyrba, Martin},
      title        = {{A}n unsupervised {XAI} framework for dementia detection
                      with context enrichment.},
      journal      = {Scientific reports},
      volume       = {15},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DZNE-2025-01261},
      pages        = {39554},
      year         = {2025},
      abstract     = {Explainable Artificial Intelligence (XAI) methods enhance
                      the diagnostic efficiency of clinical decision support
                      systems by making the predictions of a convolutional neural
                      network's (CNN) on brain imaging more transparent and
                      trustworthy. However, their clinical adoption is limited due
                      to limited validation of the explanation quality. Our study
                      introduces a framework that evaluates XAI methods by
                      integrating neuroanatomical morphological features with
                      CNN-generated relevance maps for disease classification. We
                      trained a CNN using brain MRI scans from six cohorts: ADNI,
                      AIBL, DELCODE, DESCRIBE, EDSD, and NIFD (N = 3253),
                      including participants that were cognitively normal, with
                      amnestic mild cognitive impairment, dementia due to
                      Alzheimer's disease and frontotemporal dementia. Clustering
                      analysis benchmarked different explanation space
                      configurations by using morphological features as
                      proxy-ground truth. We implemented three post-hoc
                      explanations methods: (i) by simplifying model decisions,
                      (ii) explanation-by-example, and (iii) textual explanations.
                      A qualitative evaluation by clinicians (N = 6) was performed
                      to assess their clinical validity. Clustering performance
                      improved in morphology enriched explanation spaces,
                      improving both homogeneity and completeness of the clusters.
                      Post hoc explanations by model simplification largely
                      delineated converters and stable participants, while
                      explanation-by-example presented possible cognition
                      trajectories. Textual explanations gave rule-based
                      summarization of pathological findings. Clinicians'
                      qualitative evaluation highlighted challenges and
                      opportunities of XAI for different clinical applications.
                      Our study refines XAI explanation spaces and applies various
                      approaches for generating explanations. Within the context
                      of AI-based decision support system in dementia research we
                      found the explanations methods to be promising towards
                      enhancing diagnostic efficiency, backed up by the clinical
                      assessments.},
      keywords     = {Humans / Dementia: diagnosis / Dementia: diagnostic imaging
                      / Magnetic Resonance Imaging: methods / Male / Female / Aged
                      / Cognitive Dysfunction: diagnostic imaging / Cognitive
                      Dysfunction: diagnosis / Alzheimer Disease: diagnostic
                      imaging / Alzheimer Disease: diagnosis / Neural Networks,
                      Computer / Artificial Intelligence / Brain: diagnostic
                      imaging / Brain: pathology / Aged, 80 and over /
                      Alzheimer’s disease (Other) / Brain volumetry (Other) /
                      Explainable artificial intelligence (XAI) (Other) /
                      Frontotemporal dementia (Other) / Magnetic resonance imaging
                      (Other) / Neurodegenerative diseases (Other) / Qualitative
                      evaluation (Other)},
      cin          = {AG Teipel / AG Hermann / Clinical Research (Munich) / AG
                      Düzel / Patient Studies (Bonn) / AG Peters / Clinical
                      Research (Bonn) / AG Gasser / AG Dichgans / AG Priller / AG
                      Spottke / AG Schneider / AG Fischer / AG Wiltfang / AG
                      Jessen},
      ddc          = {600},
      cid          = {I:(DE-2719)1510100 / I:(DE-2719)1511100 /
                      I:(DE-2719)1111015 / I:(DE-2719)5000006 / I:(DE-2719)1011101
                      / I:(DE-2719)5000000 / I:(DE-2719)1011001 /
                      I:(DE-2719)1210000 / I:(DE-2719)5000022 / I:(DE-2719)5000007
                      / I:(DE-2719)1011103 / I:(DE-2719)1011305 /
                      I:(DE-2719)1410002 / I:(DE-2719)1410006 /
                      I:(DE-2719)1011102},
      pnm          = {353 - Clinical and Health Care Research (POF4-353) / 352 -
                      Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-353 / G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41224940},
      pmc          = {pmc:PMC12612133},
      doi          = {10.1038/s41598-025-26227-2},
      url          = {https://pub.dzne.de/record/282290},
}