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@ARTICLE{Walger:277419,
      author       = {Walger, Lennart and Bauer, Tobias and Kügler, David and
                      Schmitz, Matthias H and Schuch, Fabiane and Arendt,
                      Christophe and Baumgartner, Tobias and Birkenheier, Johannes
                      and Borger, Valeri and Endler, Christoph and Grau, Franziska
                      and Immanuel, Christian and Kölle, Markus and Kupczyk,
                      Patrick and Lakghomi, Asadeh and Mackert, Sarah and Neuhaus,
                      Elisabeth and Nordsiek, Julia and Odenthal, Anna-Maria and
                      Dague, Karmele Olaciregui and Ostermann, Laura and
                      Pukropski, Jan and Racz, Attila and von der Ropp, Klaus and
                      Schmeel, Frederic Carsten and Schrader, Felix and Sitter,
                      Aileen and Unruh-Pinheiro, Alexander and Voigt, Marilia and
                      Vychopen, Martin and von Wedel, Philip and von Wrede, Randi
                      and Attenberger, Ulrike and Vatter, Hartmut and Philipsen,
                      Alexandra and Becker, Albert and Reuter, Martin and
                      Hattingen, Elke and Sander, Josemir W and Radbruch,
                      Alexander and Surges, Rainer and Rüber, Theodor},
      title        = {{A} {Q}uantitative {C}omparison {B}etween {H}uman and
                      {A}rtificial {I}ntelligence in the {D}etection of {F}ocal
                      {C}ortical {D}ysplasia.},
      journal      = {Investigative radiology},
      volume       = {60},
      number       = {4},
      issn         = {0020-9996},
      address      = {Philadelphia, Pa.},
      publisher    = {Lippincott Williams $\&$ Wilkins},
      reportid     = {DZNE-2025-00402},
      pages        = {253 - 259},
      year         = {2025},
      abstract     = {Artificial intelligence (AI) is thought to improve lesion
                      detection. However, a lack of knowledge about human
                      performance prevents a comparative evaluation of AI and an
                      accurate assessment of its impact on clinical
                      decision-making. The objective of this work is to
                      quantitatively evaluate the ability of humans to detect
                      focal cortical dysplasia (FCD), compare it to
                      state-of-the-art AI, and determine how it may aid
                      diagnostics.We prospectively recorded the performance of
                      readers in detecting FCDs using single points and
                      3-dimensional bounding boxes. We acquired predictions of 3
                      AI models for the same dataset and compared these to
                      readers. Finally, we analyzed pairwise combinations of
                      readers and models.Twenty-eight readers, including 20
                      nonexpert and 5 expert physicians, reviewed 180 cases: 146
                      subjects with FCD (median age: 25, interquartile range: 18)
                      and 34 healthy control subjects (median age: 43,
                      interquartile range: 19). Nonexpert readers detected $47\%$
                      $(95\%$ confidence interval [CI]: 46, 49) of FCDs, whereas
                      experts detected $68\%$ $(95\%$ CI: 65, 71). The 3 AI models
                      detected $32\%,$ $51\%,$ and $72\%$ of FCDs, respectively.
                      The latter, however, also predicted more than 13
                      false-positive clusters per subject on average. Human
                      performance was improved in the presence of a transmantle
                      sign ( P < 0.001) and cortical thickening ( P < 0.001). In
                      contrast, AI models were sensitive to abnormal gyration ( P
                      < 0.01) or gray-white matter blurring ( P < 0.01). Compared
                      with single experts, expert-expert pairs detected $13\%$
                      $(95\%$ CI: 9, 18) more FCDs ( P < 0.001). All AI models
                      increased expert detection rates by up to $19\%$ $(95\%$ CI:
                      15, 24) ( P < 0.001). Nonexpert+AI pairs could still
                      outperform single experts by up to $13\%$ $(95\%$ CI: 10,
                      17).This study pioneers the comparative evaluation of humans
                      and AI for FCD lesion detection. It shows that AI and human
                      predictions differ, especially for certain MRI features of
                      FCD, and, thus, how AI may complement the diagnostic
                      workup.},
      keywords     = {Humans / Artificial Intelligence / Female / Male / Magnetic
                      Resonance Imaging: methods / Adult / Prospective Studies /
                      Malformations of Cortical Development: diagnostic imaging /
                      Image Interpretation, Computer-Assisted: methods / Young
                      Adult / Adolescent / Sensitivity and Specificity / Middle
                      Aged / Reproducibility of Results / Focal Cortical
                      Dysplasia},
      cin          = {AG Reuter / AG Radbruch},
      ddc          = {610},
      cid          = {I:(DE-2719)1040310 / I:(DE-2719)5000075},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354) / 353
                      - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-354 / G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39437019},
      doi          = {10.1097/RLI.0000000000001125},
      url          = {https://pub.dzne.de/record/277419},
}