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000277419 037__ $$aDZNE-2025-00402
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000277419 1001_ $$aWalger, Lennart$$b0
000277419 245__ $$aA Quantitative Comparison Between Human and Artificial Intelligence in the Detection of Focal Cortical Dysplasia.
000277419 260__ $$aPhiladelphia, Pa.$$bLippincott Williams & Wilkins$$c2025
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000277419 520__ $$aArtificial 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.
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000277419 650_2 $$2MeSH$$aHumans
000277419 650_2 $$2MeSH$$aArtificial Intelligence
000277419 650_2 $$2MeSH$$aFemale
000277419 650_2 $$2MeSH$$aMale
000277419 650_2 $$2MeSH$$aMagnetic Resonance Imaging: methods
000277419 650_2 $$2MeSH$$aAdult
000277419 650_2 $$2MeSH$$aProspective Studies
000277419 650_2 $$2MeSH$$aMalformations of Cortical Development: diagnostic imaging
000277419 650_2 $$2MeSH$$aImage Interpretation, Computer-Assisted: methods
000277419 650_2 $$2MeSH$$aYoung Adult
000277419 650_2 $$2MeSH$$aAdolescent
000277419 650_2 $$2MeSH$$aSensitivity and Specificity
000277419 650_2 $$2MeSH$$aMiddle Aged
000277419 650_2 $$2MeSH$$aReproducibility of Results
000277419 650_2 $$2MeSH$$aFocal Cortical Dysplasia
000277419 7001_ $$0P:(DE-2719)9002598$$aBauer, Tobias$$b1$$udzne
000277419 7001_ $$0P:(DE-2719)2814290$$aKügler, David$$b2$$udzne
000277419 7001_ $$0P:(DE-HGF)0$$aSchmitz, Matthias H$$b3
000277419 7001_ $$aSchuch, Fabiane$$b4
000277419 7001_ $$aArendt, Christophe$$b5
000277419 7001_ $$aBaumgartner, Tobias$$b6
000277419 7001_ $$aBirkenheier, Johannes$$b7
000277419 7001_ $$aBorger, Valeri$$b8
000277419 7001_ $$aEndler, Christoph$$b9
000277419 7001_ $$aGrau, Franziska$$b10
000277419 7001_ $$aImmanuel, Christian$$b11
000277419 7001_ $$aKölle, Markus$$b12
000277419 7001_ $$aKupczyk, Patrick$$b13
000277419 7001_ $$aLakghomi, Asadeh$$b14
000277419 7001_ $$aMackert, Sarah$$b15
000277419 7001_ $$aNeuhaus, Elisabeth$$b16
000277419 7001_ $$0P:(DE-2719)9001884$$aNordsiek, Julia$$b17
000277419 7001_ $$aOdenthal, Anna-Maria$$b18
000277419 7001_ $$aDague, Karmele Olaciregui$$b19
000277419 7001_ $$aOstermann, Laura$$b20
000277419 7001_ $$aPukropski, Jan$$b21
000277419 7001_ $$aRacz, Attila$$b22
000277419 7001_ $$avon der Ropp, Klaus$$b23
000277419 7001_ $$0P:(DE-2719)9001551$$aSchmeel, Frederic Carsten$$b24
000277419 7001_ $$aSchrader, Felix$$b25
000277419 7001_ $$aSitter, Aileen$$b26
000277419 7001_ $$aUnruh-Pinheiro, Alexander$$b27
000277419 7001_ $$aVoigt, Marilia$$b28
000277419 7001_ $$aVychopen, Martin$$b29
000277419 7001_ $$avon Wedel, Philip$$b30
000277419 7001_ $$avon Wrede, Randi$$b31
000277419 7001_ $$aAttenberger, Ulrike$$b32
000277419 7001_ $$aVatter, Hartmut$$b33
000277419 7001_ $$aPhilipsen, Alexandra$$b34
000277419 7001_ $$aBecker, Albert$$b35
000277419 7001_ $$0P:(DE-2719)2812134$$aReuter, Martin$$b36$$udzne
000277419 7001_ $$aHattingen, Elke$$b37
000277419 7001_ $$aSander, Josemir W$$b38
000277419 7001_ $$0P:(DE-2719)9001861$$aRadbruch, Alexander$$b39$$udzne
000277419 7001_ $$aSurges, Rainer$$b40
000277419 7001_ $$00000-0002-6180-7671$$aRüber, Theodor$$b41
000277419 773__ $$0PERI:(DE-600)2041543-6$$a10.1097/RLI.0000000000001125$$gVol. 60, no. 4, p. 253 - 259$$n4$$p253 - 259$$tInvestigative radiology$$v60$$x0020-9996$$y2025
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