Deep learning-based detection of focal cortical dysplasia in children: External validation of the MELD Graph and 3D-nnUNet pipelines.
Authors
Affiliations (10)
Affiliations (10)
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Berlin, Institute of Neuroradiology, Berlin, Germany.
- MR Physics Group. Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom.
- Developmental Imaging and Biophysics Section, Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany.
- Department of Epileptology, University Hospital Bonn, Bonn, Germany.
- Institute for Computer Science, University of Bonn, Bonn, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
- Center for Medical Data Usability and Translation (ZMDT), University of Bonn, Bonn, Germany.
- Center for Chronically Sick Children, Institute of Cell Biology and Neurobiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Berlin, Dept. of Pediatric Neurology, Berlin, Germany.
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Berlin, Dept. of Pediatric Neurosurgery, Berlin, Germany.
Abstract
To externally validate two recently developed deep-learning approaches for FCD detection, MELD Graph and 3D-nnUNet, in a pediatric cohort. Focal cortical dysplasias (FCDs) are among the most common structural causes of drug-resistant epilepsy in children but are frequently subtle and difficult to detect on MRI. Several automated lesion detection methods have been proposed to support neuroradiological assessment. We externally validated two recently developed deep-learning approaches for FCD detection, MELD Graph and 3D-nnUNet, in a pediatric cohort. In this retrospective single-center study, we analyzed brain MRI scans of 70 children evaluated for epilepsy, including 34 MRI-positive patients with suspected FCD and 36 MRI-negative patients, based on primary radiology reports. Both models were applied to 3D T1-weighted and 3D FLAIR images. The detected lesions were reviewed by an experienced pediatric neuroradiologist and classified as true positive, false positive, or false negative. False-positive cases were additionally reviewed using electroclinical findings. MELD Graph and 3D-nnUNet achieved comparable lesion precision (0.86 vs. 0.90) but only moderate recall (0.63 vs. 0.47). At the patient level, MELD Graph demonstrated higher sensitivity (0.71 vs. 0.53), whereas 3D-nnUNet achieved substantially higher specificity (0.86 vs. 0.47) owing to fewer false-positive detections. 3D-nnUNet showed higher sensitivity with the improved FLAIR sequence, whereas MELD Graph performance was strongly influenced by the accuracy of cortical surface reconstruction. Both models failed to detect a clinically relevant proportion of FCDs. MELD Graph and 3D-nnUNet generalize to pediatric MRI and represent valuable decision-support tools for highlighting suspicious cortical regions. However, their moderate sensitivity and susceptibility to false-positive findings indicate that they should complement, rather than replace, expert neuroradiological interpretation.