ArticleBrain communications2024
Adding the third dimension: 3D convolutional neural network diagnosis of temporal lobe epilepsy.
Article in Brain communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Explainable Deep Learning for MRI-Negative Temporal Lobe Epilepsy: Classification and Brain Region Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- AI enabled decision support systems in epilepsy surgery a scoping review.Research square · 2026Article
- Deep learning enabled decision support systems in epilepsy surgery: a scoping review.npj health systems · 2026Article
- Disease detection and classification in temporal lobe epilepsy: step-wise versus simultaneous AI decision models in a multisite neuroimaging study.Brain communications · 2026Article
- Imaging Epilepsy: Past, Passing, and to Come.Epilepsy currents · 2025Review
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Authors and funding
19 authors.
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Abstract
Convolutional neural networks (CNN) show great promise for translating decades of research on structural abnormalities in temporal lobe epilepsy into clinical practice. Three-dimensional CNNs typically outperform two-dimensional CNNs in medical imaging. Here we explore for the first time whether a three-dimensional CNN outperforms a two-dimensional CNN for identifying temporal lobe epilepsy-specific features on MRI. Using 1178 T1-weighted images (589 temporal lobe epilepsy, 589 healthy controls) from 12 surgical centres, we trained 3D and 2D CNNs for temporal lobe epilepsy versus healthy control classification, using feature visualization to identify important regions. The 3D CNN was compared to the 2D model and to a randomized model (comparison to chance). Further, we explored the effect of sample size with subsampling, examined model performance based on single-subject clinical characteristics, and tested the impact of image harmonization on model performance. Across 50 datapoints (10 runs with 5-folds each) the 3D CNN median accuracy was 86.4% (35.3% above chance) and the median
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