ArticleIEEE transactions on bio-medical engineering2025
Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy.
Article in IEEE transactions on bio-medical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Network-based biomarkers in pediatric drug-resistant epilepsy.Clinical and experimental pediatrics · 2026Article
- Classification of neurocognitive impairment in pediatric drug-resistant focal epilepsy by quantifying seizure-affected brain network abnormalities in clinical diffusion-weighted imaging connectome.Clinical and experimental pediatrics · 2026Article
- AI enabled decision support systems in epilepsy surgery a scoping review.Research square · 2026Article
- Diffusion Tractography Biomarker for Epilepsy Severity in Children With Drug-Resistant Epilepsy.Annals of clinical and translational neurology · 2026Article
- Deep learning enabled decision support systems in epilepsy surgery: a scoping review.npj health systems · 2026Article
- Multimodal artificial intelligence in epilepsy: antiseizure medication response, epileptogenic zone localization, and surgical outcome prediction.Frontiers in neurology · 2026Review
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9 authors.
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Abstract
objectiveTo develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modular networks (LMNs) within the preoperative whole-brain diffusion-weighted imaging connectome (wDWIC).
methodsWe employed a three-step approach. First, our previous DCNN-based tract classification to detect true-positive eloquent tracts was extended using an open-source database of high-quality wDWIC to facilitate the accurate classification of true-positive tracts within the preoperative backbone wDWIC of individual patients. Next, we applied psychometry-driven DWIC analysis to the resulting DCNN-based backbone wDWIC in order to create core, expressive, and receptive LMNs. Finally, graph and circuit theory-based connectivity markers were assessed within the three LMNs and compared using a series of machine learning algorithms to predict the presence of postoperative language improvement from a given LMN.
resultsThe results showed that the extended DCNN tract classification significantly improved the reproducibility of connectivity markers by up to 35.5 of F-statistics across different LMNs. The prediction accuracy increased by up to 40 across different machine learning algorithms. Notably, the best algorithm achieved the accuracy of 96/94/96 to predict the presence of language improvement about two months after surgery in core/expressive/receptive domain of an independent validation cohort.
conclusionThese domains hold great potential to assist physicians in identifying candidates whose language skills stand to benefit from early surgery. SIGNIFICANCE: DCNN tract classification may be an effective tool to improve predicting short-term postoperative language improvement in pediatric epilepsy surgery.
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