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@ARTICLE{Singh:281857,
      author       = {Singh, Devesh and Grazia, Alice and Reiz, Achim and
                      Hermann, Andreas and Altenstein, Slawek and Beichert, Lukas
                      and Bernhardt, Alexander and Buerger, Katharina and Butryn,
                      Michaela and Dechent, Peter and Duezel, Emrah and Ewers,
                      Michael and Fliessbach, Klaus and Freiesleben, Silka D and
                      Glanz, Wenzel and Hetzer, Stefan and Janowitz, Daniel and
                      Kilimann, Ingo and Kimmich, Okka and Laske, Christoph and
                      Levin, Johannes and Lohse, Andrea and Lüsebrink-Rindsland,
                      Jann Falk Silvester and Munk, Matthias and Perneczky, Robert
                      and Peters, Oliver and Preis, Lukas and Priller, Josef and
                      Prudlo, Johannes and Rauchmann, Boris Stephan and
                      Rostamzadeh, Ayda and Roy-Kluth, Nina and Scheffler, Klaus
                      and Schneider, Anja and Schneider, Luisa-Sophie and Schott,
                      Björn H and Spottke, Annika and Spruth, Eike Jakob and
                      Synofzik, Matthis and Wiltfang, Jens and Jessen, Frank and
                      Teipel, Stefan J and Dyrba, Martin},
      collaboration = {ADNI and AIBL and FTLDNI and groups, study},
      title        = {{A} computational ontology framework for the synthesis of
                      multi-level pathology reports from brain {MRI} scans.},
      journal      = {Journal of Alzheimer's disease},
      volume       = {108},
      number       = {$1_suppl$},
      issn         = {1387-2877},
      address      = {Amsterdam},
      publisher    = {IOS Press},
      reportid     = {DZNE-2025-01230},
      pages        = {S258 - S273},
      year         = {2025},
      abstract     = {BackgroundConvolutional neural network (CNN) based
                      volumetry of MRI data can help differentiate Alzheimer's
                      disease (AD) and the behavioral variant of frontotemporal
                      dementia (bvFTD) as causes of cognitive decline and
                      dementia. However, existing CNN-based MRI volumetry tools
                      lack a structured hierarchical representation of brain
                      anatomy, which would allow for aggregating regional
                      pathological information and automated computational
                      inference.ObjectiveDevelop a computational ontology pipeline
                      for quantifying hierarchical pathological abnormalities and
                      visualize summary charts for brain atrophy findings, aiding
                      differential diagnosis.MethodsUsing FastSurfer, we segmented
                      brain regions and measured volume and cortical thickness
                      from MRI scans pooled across multiple cohorts (N = 3433;
                      ADNI, AIBL, DELCODE, DESCRIBE, EDSD, and NIFD), including
                      healthy controls, prodromal and clinical AD cases, and bvFTD
                      cases. Employing the Web Ontology Language (OWL), we built a
                      semantic model encoding hierarchical anatomical information.
                      Additionally, we created summary visualizations based on
                      sunburst plots for visual inspection of the information
                      stored in the ontology.ResultsOur computational framework
                      dynamically estimated and aggregated regional pathological
                      deviations across different levels of neuroanatomy
                      abstraction. The disease similarity index derived from the
                      volumetric and cortical thickness deviations achieved an AUC
                      of 0.88 for separating AD and bvFTD, which was also
                      reflected by distinct atrophy profile
                      visualizations.ConclusionsThe proposed automated pipeline
                      facilitates visual comparison of atrophy profiles across
                      various disease types and stages. It provides a
                      generalizable computational framework for summarizing
                      pathologic findings, potentially enhancing the physicians'
                      ability to evaluate brain pathologies robustly and
                      interpretably.},
      keywords     = {Humans / Magnetic Resonance Imaging: methods / Alzheimer
                      Disease: pathology / Alzheimer Disease: diagnostic imaging /
                      Brain: pathology / Brain: diagnostic imaging / Male / Female
                      / Aged / Frontotemporal Dementia: pathology / Frontotemporal
                      Dementia: diagnostic imaging / Atrophy: pathology / Neural
                      Networks, Computer / Middle Aged / Alzheimer's disease
                      (Other) / brain volumetry (Other) / computer graphics
                      (Other) / frontotemporal dementia (Other) / magnetic
                      resonance imaging (Other) / neuroanatomy (Other) / ontology
                      (Other)},
      cin          = {AG Teipel / AG Spottke / Clinical Research Platform (CRP) /
                      AG Hermann / AG Priller / Clinical Research (Munich) / AG
                      Düzel / Patient Studies (Bonn) / AG Peters / Clinical
                      Research (Bonn) / AG Gasser / AG Levin / AG Dichgans / AG
                      Schneider / AG Jessen / AG Wiltfang},
      ddc          = {610},
      cid          = {I:(DE-2719)1510100 / I:(DE-2719)1011103 /
                      I:(DE-2719)1011401 / I:(DE-2719)1511100 / I:(DE-2719)5000007
                      / 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)1111016 /
                      I:(DE-2719)5000022 / I:(DE-2719)1011305 / I:(DE-2719)1011102
                      / I:(DE-2719)1410006},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40255031},
      pmc          = {pmc:PMC12583655},
      doi          = {10.1177/13872877251331222},
      url          = {https://pub.dzne.de/record/281857},
}