English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Focal cortical dysplasia (FCD) is a leading cause of drug-resistant focalepilepsy, which can be cured by surgery. These lesions are extremely subtle andoften missed even by expert neuroradiologists. "Ground truth" manual lesionmasks are therefore expensive, limited and have large inter-rater variability.Existing FCD detection methods are limited by high numbers of false positivepredictions, primarily due to vertex- or patch-based approaches that lackwhole-brain context. Here, we propose to approach the problem as semanticsegmentation using graph convolutional networks (GCN), which allows our modelto learn spatial relationships between brain regions. To address the specificchallenges of FCD identification, our proposed model includes an auxiliary lossto predict distance from the lesion to reduce false positives and a weaksupervision classification loss to facilitate learning from uncertain lesionmasks. On a multi-centre dataset of 1015 participants with surface-basedfeatures and manual lesion masks from structural MRI data, the proposed GCNachieved an AUC of 0.74, a significant improvement against a previously usedvertex-wise multi-layer perceptron (MLP) classifier (AUC 0.64). Withsensitivity thresholded at 67%, the GCN had a specificity of 71% in comparisonto 49% when using the MLP. This improvement in specificity is vital forclinical integration of lesion-detection tools into the radiological workflow,through increasing clinical confidence in the use of AI radiological adjunctsand reducing the number of areas requiring expert review.

Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach