ArticleNeuroimage. Reports2026
Deep learning-based detection of focal cortical dysplasia in children: External validation of the MELD Graph and 3D-nnUNet pipelines.
Article in Neuroimage. Reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To externally validate two recently developed deep-learning approaches for FCD detection, MELD Graph and 3D-nnUNet, in a pediatric cohort. Background: 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. Methods: 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. Results: 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. Conclusion: 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.