Artificial intelligence for the detection of focal cortical dysplasia: Challenges in translating algorithms into clinical practice

• Published in: Epilepsia (Copenhagen) 2023-05, Vol.64 (5), p.1093-1112
• Main Authors: Walger, Lennart, Adler, Sophie, Wagstyl, Konrad, Henschel, Leonie, David, Bastian, Borger, Valeri, Hattingen, Elke, Vatter, Hartmut, Elger, Christian E., Baldeweg, Torsten, Rosenow, Felix, Urbach, Horst, Becker, Albert, Radbruch, Alexander, Surges, Rainer, Reuter, Martin, Cendes, Fernando, Wang, Zhong Irene, Huppertz, Hans‐Jürgen, Rüber, Theodor
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; digitalization in medicine; Dysplasia; Epilepsy; Epilepsy - diagnostic imaging; Epilepsy - surgery; Focal Cortical Dysplasia; focal epilepsy; Humans; image processing; Magnetic resonance imaging; Neuroimaging; Neurosurgery; Patients; presurgical evaluation
• ISSN: 0013-9580; 1528-1167
• DOI: 10.1111/epi.17522

Focal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common pathologies causing pharmacoresistant focal epilepsy. Resective neurosurgery yields high success rates, especially if the full extent of the lesion is correctly identified and completely removed. The visual assessment of magnetic resonance imaging does not pinpoint the FCD in 30%–50% of cases, and half of all patients with FCD are not amenable to epilepsy surgery, partly because the FCD could not be sufficiently localized. Computational approaches to FCD detection are an active area of research, benefitting from advancements in computer vision. Automatic FCD detection is a significant challenge and one of the first clinical grounds where the application of artificial intelligence may translate into an advance for patients' health. The emergence of new methods from the combination of health and computer sciences creates novel challenges. Imaging data need to be organized into structured, well‐annotated datasets and combined with other clinical information, such as histopathological subtypes or neuroimaging characteristics. Algorithmic output, that is, model prediction, requires a technically correct evaluation with adequate metrics that are understandable and usable for clinicians. Publication of code and data is necessary to make research accessible and reproducible. This critical review introduces the field of automatic FCD detection, explaining underlying medical and technical concepts, highlighting its challenges and current limitations, and providing a perspective for a novel research environment.