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@ARTICLE{Walger:279188,
      author       = {Walger, Lennart and Schmitz, Matthias H and Bauer, Tobias
                      and Kügler, David 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 Radbruch, Alexander and Surges, Rainer
                      and Rüber, Theodor},
      title        = {{A} public benchmark for human performance in the detection
                      of focal cortical dysplasia.},
      journal      = {Epilepsia open},
      volume       = {10},
      number       = {3},
      issn         = {2470-9239},
      address      = {Hoboken, NJ},
      publisher    = {Wiley},
      reportid     = {DZNE-2025-00716},
      pages        = {778 - 786},
      year         = {2025},
      abstract     = {This study aims to report human performance in the
                      detection of Focal Cortical Dysplasias (FCDs) using an
                      openly available dataset. Additionally, it defines a subset
                      of this data as a 'difficult' test set to establish a public
                      baseline benchmark against which new methods for automated
                      FCD detection can be evaluated.The performance of 28 human
                      readers with varying levels of expertise in detecting FCDs
                      was originally analyzed using 146 subjects (not all of which
                      are openly available), we analyzed the openly available
                      subset of 85 cases. Performance was measured based on the
                      overlap between predicted regions of interest (ROIs) and
                      ground-truth lesion masks, using the Dice-Soerensen
                      coefficient (DSC). The benchmark test set was chosen to
                      consist of 15 subjects most predictive for human performance
                      and 13 subjects identified by at most 3 of the 28
                      readers.Expert readers achieved an average detection rate of
                      $68\%,$ compared to $45\%$ for non-experts and $27\%$ for
                      laypersons. Neuroradiologists detected the highest
                      percentage of lesions $(64\%),$ while psychiatrists detected
                      the least $(34\%).$ Neurosurgeons had the highest ROI
                      sensitivity (0.70), and psychiatrists had the highest ROI
                      precision (0.78). The benchmark test set revealed an expert
                      detection rate of $49\%.Reporting$ human performance in FCD
                      detection provides a critical baseline for assessing the
                      effectiveness of automated detection methods in a clinically
                      relevant context. The defined benchmark test set serves as a
                      useful indicator for evaluating advancements in
                      computer-aided FCD detection approaches.Focal cortical
                      dysplasias (FCDs) are malformations of cortical development
                      and one of the most common causes of drug-resistant focal
                      epilepsy. Once found, FCDs can be neurosurgically resected,
                      which leads to seizure freedom in many cases. However, FCDs
                      are difficult to detect in the visual assessment of magnetic
                      resonance imaging. A myriad of algorithms for automated FCD
                      detection have been developed, but their true clinical value
                      remains unclear since there is no benchmark dataset for
                      evaluation and comparison to human performance. Here, we use
                      human FCD detection performance to define a benchmark
                      dataset with which new methods for automated detection can
                      be evaluated.},
      keywords     = {Humans / Benchmarking / Malformations of Cortical
                      Development: diagnostic imaging / Malformations of Cortical
                      Development: diagnosis / Magnetic Resonance Imaging / Female
                      / Male / Adult / Focal Cortical Dysplasia / artificial
                      intelligence (Other) / computer‐aided detection (Other) /
                      human performance (Other) / reader study (Other)},
      cin          = {AG Reuter / AG Stöcker / AG Radbruch},
      ddc          = {610},
      cid          = {I:(DE-2719)1040310 / I:(DE-2719)1013026 /
                      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:40167314},
      doi          = {10.1002/epi4.70028},
      url          = {https://pub.dzne.de/record/279188},
}