Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy

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Min-Hee Lee, Soumyanil Banerjee, Hiroshi Uda, Alanna Carlson, Ming Dong, Robert Rothermel, Csaba Juhász, Eishi Asano, Jeong-Won Jeong

Published: 2025, Last Modified: 15 Jan 2026IEEE Trans. Biomed. Eng. 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Objective: To 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). Methods: We 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. Results: The 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. Conclusion: These 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.