Can neural networks support identification of epileptogenic foci in children with suspected focal cortical dysplasia (FCD)?

Motivation: Success of surgical resection in focal epilepsy is highly dependent on identification of epileptogenic foci. Machine learning algorithms are proving useful in supporting detection of pediatric focal cortical dysplasia (FCD) lesions. Goal(s): Evaluating 2 algorithms predicting FCD lesions, developed by the multi-centre epilepsy lesion-detection project (MELD): the multi-layer-perceptron-based classifier [MELD_MLP] and the newer graph-neural-network based algorithm [MELD_GRAPH] aiming to increase specificity/accuracy. Is the new model better for routine clinical integration? Approach: Comparison of true and false-positive rates for MELD_Graph vs MELD_MLP on 17 children Results: MELD_MLP detects more lesions than MELD_Graph. However MELD_Graph produces less false positives, resulting in better positive predictive value Impact: Clinical validation is essential for AI-based Focal Cortical Dysplasia lesion-detection algorithms. A local trade-off between sensitivity and specificity is necessary when selecting an appropriate decision support AI/machine-learning tool.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/4121
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
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article

Can neural networks support identification of epileptogenic foci in children with suspected focal cortical dysplasia (FCD)?

Judith Helen Cross, Friederike Moeller, Martin Tisdall, Ajai Chari et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Brain Tumor Detection and Classification
article

Can neural networks support identification of epileptogenic foci in children with suspected focal cortical dysplasia (FCD)?

Judith Helen Cross, Friederike Moeller, Martin Tisdall, Ajai Chari, Muhammad Zubair Tahir, Ulrike Loebel, Sniya Sudhakar, Suresh Pujar, Mathilde Ripart, Enrico De Vita, Annemarie Knill, Pritika Gaur, Sophie Adler, Felice D’Arco, Asthik Biswas, Konrad Wagstyl, Kshitij Mankad, Yi Li, Kiran Senaurine
article en

Abstract

Motivation: Success of surgical resection in focal epilepsy is highly dependent on identification of epileptogenic foci. Machine learning algorithms are proving useful in supporting detection of pediatric focal cortical dysplasia (FCD) lesions. Goal(s): Evaluating 2 algorithms predicting FCD lesions, developed by the multi-centre epilepsy lesion-detection project (MELD): the multi-layer-perceptron-based classifier [MELD_MLP] and the newer graph-neural-network based algorithm [MELD_GRAPH] aiming to increase specificity/accuracy. Is the new model better for routine clinical integration? Approach: Comparison of true and false-positive rates for MELD_Graph vs MELD_MLP on 17 children Results: MELD_MLP detects more lesions than MELD_Graph. However MELD_Graph produces less false positives, resulting in better positive predictive value Impact: Clinical validation is essential for AI-based Focal Cortical Dysplasia lesion-detection algorithms. A local trade-off between sensitivity and specificity is necessary when selecting an appropriate decision support AI/machine-learning tool.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 69%
Brain Tumor Detection and Classification
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