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@ARTICLE{Kersting:278020,
      author       = {Kersting, Lennart N and Walger, Lennart and Bauer, Tobias
                      and Gnatkovsky, Vadym and Schuch, Fabiane and David, Bastian
                      and Neuhaus, Elisabeth and Keil, Fee and Tietze, Anna and
                      Rosenow, Felix and Kaindl, Angela M and Hattingen, Elke and
                      Huppertz, Hans-Jürgen and Radbruch, Alexander and Surges,
                      Rainer and Rüber, Theodor},
      title        = {{D}etection of focal cortical dysplasia: {D}evelopment and
                      multicentric evaluation of artificial intelligence models.},
      journal      = {Epilepsia},
      volume       = {66},
      number       = {4},
      issn         = {0013-9580},
      address      = {Oxford [u.a.]},
      publisher    = {Wiley-Blackwell},
      reportid     = {DZNE-2025-00530},
      pages        = {1165 - 1176},
      year         = {2025},
      abstract     = {Focal cortical dysplasia (FCD) is a common cause of
                      drug-resistant focal epilepsy but can be challenging to
                      detect visually on magnetic resonance imaging. Three
                      artificial intelligence models for automated FCD detection
                      are publicly available (MAP18, deepFCD, MELD) but have only
                      been compared on single-center data. Our first objective is
                      to compare them on independent multicenter test data.
                      Additionally, we train and compare three new models and make
                      them publicly available.We retrospectively collected FCD
                      cases from four epilepsy centers. We chose three novel
                      models that take two-dimensional (2D) slices (2D-nnUNet),
                      2.5D slices (FastSurferCNN), and large 3D patches
                      (3D-nnUNet) as inputs and trained them on a subset of Bonn
                      data. As core evaluation metrics, we used voxel-level Dice
                      similarity coefficient (DSC), cluster-level F1 score,
                      subject-level detection rate, and specificity.We collected
                      329 subjects, 244 diagnosed with FCD (27.7 ± 14.4 years
                      old, $54\%$ male) and 85 healthy controls (7.1 ± 2.4 years
                      old, $51\%$ female). We used 118 subjects for model training
                      and kept the remaining subjects as an independent test set.
                      3D-nnUNet achieved the highest F1 score of .58, the highest
                      DSC of .36 $(95\%$ confidence interval [CI] = .30-.41), a
                      detection rate of $55\%,$ and a specificity of $86\%.$
                      deepFCD showed the highest detection rate $(82\%)$ but had
                      the lowest specificity $(0\%)$ and cluster-level precision
                      (.03, $95\%$ CI = .03-.04, F1 score = .07). MELD showed the
                      least performance variation across centers, with detection
                      rates between $46\%$ and $54\%.This$ study shows the
                      variance in performance for FCD detection models in a
                      multicenter dataset. The two models with 3D input data
                      showed the highest sensitivity. The 2D models performed
                      worse than all other models, suggesting that FCD detection
                      requires 3D data. The greatly improved precision of
                      3D-nnUNet may make it a sensible choice to aid FCD
                      detection.},
      keywords     = {Humans / Male / Female / Malformations of Cortical
                      Development: diagnostic imaging / Adult / Magnetic Resonance
                      Imaging: methods / Retrospective Studies / Young Adult /
                      Child / Artificial Intelligence / Adolescent / Imaging,
                      Three-Dimensional / Focal Cortical Dysplasia / MRI (Other) /
                      computer‐aided detection (Other) / epilepsy (Other) /
                      lesion detection (Other) / model comparison (Other)},
      cin          = {AG Radbruch / AG Stöcker},
      ddc          = {610},
      cid          = {I:(DE-2719)5000075 / I:(DE-2719)1013026},
      pnm          = {353 - Clinical and Health Care Research (POF4-353) / 354 -
                      Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-353 / G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39739580},
      pmc          = {pmc:PMC11997906},
      doi          = {10.1111/epi.18240},
      url          = {https://pub.dzne.de/record/278020},
}