ArticleFrontiers in human neuroscience2021
Automatic Detection of Focal Cortical Dysplasia Type II in MRI: Is the Application of Surface-Based Morphometry and Machine Learning Promising?
Article in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Multimodal artificial intelligence in epilepsy: antiseizure medication response, epileptogenic zone localization, and surgical outcome prediction.Frontiers in neurology · 2026Review
- The Expanding Frontier: The Role of Artificial Intelligence in Pediatric Neuroradiology.Children (Basel, Switzerland) · 2025Review
- Integrating standard epilepsy protocol, ASL-perfusion, MP2RAGE/EDGE and the MELD-FCD classifier in the detection of subtle epileptogenic lesions: a 3 Tesla MRI pilot study.Neuroradiology · 2025Article
- A nnU-Net-based automatic segmentation of FCD type II lesions in 3D FLAIR MRI images.Frontiers in artificial intelligence · 2025Article
- Advances in magnetic resonance imaging for the assessment of paediatric focal epilepsy: a narrative review.Translational pediatrics · 2024Review
- Focal cortical dysplasia (type II) detection with multi-modal MRI and a deep-learning framework.Npj imaging · 2024Article
- Article
- Volume-based and Surface-Based Methods in Autism Compared with Healthy Controls Are Free surfer and CAT12 in Agreement?Iranian journal of child neurology · 2024Article
- An Assessment of the Pathological Classification and Postoperative Outcome of Focal Cortical Dysplasia by Simultaneous Hybrid PET/MRI.Brain sciences · 2023Article
- Detecting Cortical Thickness Changes in Epileptogenic Lesions Using Machine Learning.Brain sciences · 2023Article
- Dysplasia and tumor discrimination in brain tissues by combined fluorescence, Raman, and diffuse reflectance spectroscopies.Biomedical optics express · 2023Article
- Machine learning in neuroimaging: from research to clinical practice.Radiologie (Heidelberg, Germany) · 2022Review
- Brain pathological changes during neurodegenerative diseases and their identification methods: How does QSM perform in detecting this process?Insights into imaging · 2022Review
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
BACKGROUND AND
objectivesFocal cortical dysplasia (FCD) is a type of malformations of cortical development and one of the leading causes of drug-resistant epilepsy. Postoperative results improve the diagnosis of lesions on structural MRIs. Advances in quantitative algorithms have increased the identification of FCD lesions. However, due to significant differences in size, shape, and location of the lesion in different patients and a big deal of time for the objective diagnosis of lesion as well as the dependence of individual interpretation, sensitive approaches are required to address the challenge of lesion diagnosis. In this research, a FCD computer-aided diagnostic system to improve existing methods is presented.
methodsMagnetic resonance imaging (MRI) data were collected from 58 participants (30 with histologically confirmed FCD type II and 28 without a record of any neurological prognosis). Morphological and intensity-based features were calculated for each cortical surface and inserted into an artificial neural network. Statistical examinations evaluated classifier efficiency.
resultsNeural network evaluation metrics-sensitivity, specificity, and accuracy-were 96.7, 100, and 98.6%, respectively. Furthermore, the accuracy of the classifier for the detection of the lobe and hemisphere of the brain, where the FCD lesion is located, was 84.2 and 77.3%, respectively.
conclusionAnalyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.
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