%0 Journal Article
%A Walger, Lennart
%A Bauer, Tobias
%A Kügler, David
%A Schmitz, Matthias H
%A Schuch, Fabiane
%A Arendt, Christophe
%A Baumgartner, Tobias
%A Birkenheier, Johannes
%A Borger, Valeri
%A Endler, Christoph
%A Grau, Franziska
%A Immanuel, Christian
%A Kölle, Markus
%A Kupczyk, Patrick
%A Lakghomi, Asadeh
%A Mackert, Sarah
%A Neuhaus, Elisabeth
%A Nordsiek, Julia
%A Odenthal, Anna-Maria
%A Dague, Karmele Olaciregui
%A Ostermann, Laura
%A Pukropski, Jan
%A Racz, Attila
%A von der Ropp, Klaus
%A Schmeel, Frederic Carsten
%A Schrader, Felix
%A Sitter, Aileen
%A Unruh-Pinheiro, Alexander
%A Voigt, Marilia
%A Vychopen, Martin
%A von Wedel, Philip
%A von Wrede, Randi
%A Attenberger, Ulrike
%A Vatter, Hartmut
%A Philipsen, Alexandra
%A Becker, Albert
%A Reuter, Martin
%A Hattingen, Elke
%A Sander, Josemir W
%A Radbruch, Alexander
%A Surges, Rainer
%A Rüber, Theodor
%T A Quantitative Comparison Between Human and Artificial Intelligence in the Detection of Focal Cortical Dysplasia.
%J Investigative radiology
%V 60
%N 4
%@ 0020-9996
%C Philadelphia, Pa.
%I Lippincott Williams & Wilkins
%M DZNE-2025-00402
%P 253 - 259
%D 2025
%X 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
%K Humans
%K Artificial Intelligence
%K Female
%K Male
%K Magnetic Resonance Imaging: methods
%K Adult
%K Prospective Studies
%K Malformations of Cortical Development: diagnostic imaging
%K Image Interpretation, Computer-Assisted: methods
%K Young Adult
%K Adolescent
%K Sensitivity and Specificity
%K Middle Aged
%K Reproducibility of Results
%K Focal Cortical Dysplasia
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:39437019
%R 10.1097/RLI.0000000000001125
%U https://pub.dzne.de/record/277419