A Quantitative Comparison Between Human and Artificial Intelligence in the Detection of Focal Cortical Dysplasia

作者：Lennart Walger, Tobias Bauer, David Kügler, Matthias Schmitz, Fabiane Schuch, Christophe Arendt, Tobias Baumgartner, Johannes Birkenheier, Valeri Borger, Christoph Endler, Franziska Grau, Christian Immanuel, Markus Kölle, Patrick Kupczyk, Asadeh Lakghomi, Sarah Mackert, Elisabeth Neuhaus, Julia Nordsiek, Anna‐Maria Odenthal, Karmele Olaciregui Dague, Laura Ostermann, Jan Pukropski, Áttila Rácz, Klaus von der Ropp, Frederic Carsten Schmeel, Felix Schrader, Aileen Sitter, Alexander Unruh-Pinheiro, Marilia Voigt, Martin Vychopen, Philip von Wedel, Randi von Wrede, Ulrike Attenberger, Hartmut Vatter, Alexandra Philipsen, Albert J. Becker, Martin Reuter, Elke Hattingen, Josemir W. Sander, Alexander Radbruch, Rainer Surges, Theodor Rüber · 发表于：Investigative Radiology · 年份：2024 · DOI：10.1097/rli.0000000000001125 · 被引用次数：10 · 研究领域：Epilepsy research and treatment、Glioma Diagnosis and Treatment、Fetal and Pediatric Neurological Disorders

OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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 <...