ArticleEpilepsia2025
Detection of focal cortical dysplasia: Development and multicentric evaluation of artificial intelligence models.
Article in Epilepsia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Deep learning-based detection of focal cortical dysplasia in children: External validation of the MELD Graph and 3D-nnUNet pipelines.Neuroimage. Reports · 2026Article
- Volume-inflation registration (INFREG) for morphometric analysis of human focal cortical dysplasia type II.iScience · 2026Article
- Comparison of volume- and surface-based magnetic resonance imaging morphometry algorithms in the detection of focal cortical dysplasia.Epilepsia · 2026Article
- Structural and Quantitative MRI Signal Features in Pediatric Focal Cortical Dysplasia: Clinical Relevance.Physiological research · 2026Article
- Pipeline evaluation of a state-of-the-art AI algorithm for detection of focal cortical dysplasia: insights into potential failure sources.Brain informatics · 2026Article
- AI in epilepsy neuroimaging.Current opinion in neurology · 2026Review
- Multimodal neuroimaging fusion in presurgical evaluation of drug-resistant epilepsy: current evidence, clinical integration, and future directions.Frontiers in neuroscience · 2026Review
- Multimodal artificial intelligence in epilepsy: antiseizure medication response, epileptogenic zone localization, and surgical outcome prediction.Frontiers in neurology · 2026Review
- Advanced neuroimaging in pediatric epilepsy surgery: state of the art and future perspectives.Neuroradiology · 2026Review
- A comparative analysis of imaging-based algorithms for detecting focal cortical dysplasia type II in children.Scientific reports · 2025Article
- Detection of focal cortical dysplasia: Development and multicentric evaluation of artificial intelligence models.Epilepsia · 2025Article
- Rewiring the brain: the AI revolution in epilepsy treatment.Frontiers in neurologyArticle
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16 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveFocal 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.
methodsWe 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 F
resultsWe 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 F SIGNIFICANCE: 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.
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