ArticleFrontiers of medicine2020
Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging.
Article in Frontiers of medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Epilepsy Detected Using a New Method Based on Volumetric Analysis Results from Brain MR Images.Biomimetics (Basel, Switzerland) · 2026Article
- Article
- Support vector machines predict postoperative memory outcomes in temporal lobe epilepsies.Epilepsia open · 2025Article
- AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.Cell reports. Medicine · 2025Review
- Prediction of Post Traumatic Epilepsy Using MR-Based Imaging Markers.Human brain mapping · 2024Article
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- Machine learning techniques based onFrontiers in neurology · 2024Article
- Adding the third dimension: 3D convolutional neural network diagnosis of temporal lobe epilepsy.Brain communications · 2024Article
- Convolutional Neural Network Algorithm to Determine Lateralization of Seizure Onset in Patients With Epilepsy: A Proof-of-Principle Study.Neurology · 2023Article
- Review
- Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease.Heliyon · 2023Article
- MRI-based deep learning can discriminate between temporal lobe epilepsy, Alzheimer's disease, and healthy controls.Communications medicine · 2023Article
- Brain but not serum BDNF levels are associated with structural alterations in the hippocampal regions in patients with drug-resistant mesial temporal lobe epilepsy.Frontiers in neuroscience · 2023Article
- Interpretable deep learning-based hippocampal sclerosis classification.Epilepsia open · 2022Article
- Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis.Heliyon · 2022Article
- Application of machine learning analysis based on diffusion tensor imaging to identify REM sleep behavior disorder.Sleep & breathing = Schlaf & Atmung · 2022Article
- Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis.Frontiers in aging neuroscience · 2022Article
- Impaired Functional Homotopy and Topological Properties Within the Default Mode Network of Children With Generalized Tonic-Clonic Seizures: A Resting-State fMRI Study.Frontiers in neuroscience · 2022Article
- Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques.Frontiers in neuroscience · 2022Article
- Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review.Frontiers in neuroscience · 2021Review
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Authors and funding
11 authors.
Funding
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Abstract
Mesial temporal lobe epilepsy (mTLE), the most common type of focal epilepsy, is associated with functional and structural brain alterations. Machine learning (ML) techniques have been successfully used in discriminating mTLE from healthy controls. However, either functional or structural neuroimaging data are mostly used separately as input, and the opportunity to combine both has not been exploited yet. We conducted a multimodal ML study based on functional and structural neuroimaging measures. We enrolled 37 patients with left mTLE, 37 patients with right mTLE, and 74 healthy controls and trained a support vector ML model to distinguish them by using each measure and the combinations of the measures. For each single measure, we obtained a mean accuracy of 74% and 69% for discriminating left mTLE and right mTLE from controls, respectively, and 64% when all patients were combined. We achieved an accuracy of 78% by integrating functional data and 79% by integrating structural data for left mTLE, and the highest accuracy of 84% was obtained when all functional and structural measures were combined. These findings suggest that combining multimodal measures within a single model is a promising direction for improving the classification of individual patients with mTLE.
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