{"doi":"10.1109/tbme.2024.3463481","title":"Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy","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.","journal":"IEEE Transactions on Biomedical Engineering","year":2024,"id":451760,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9578,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1273657,"name":"Soumyanil Banerje","orcid":null,"position":1,"is_corresponding":false},{"id":1241751,"name":"Hiroshi Uda","orcid":"0000-0002-7814-697X","position":2,"is_corresponding":false},{"id":671585,"name":"Alanna Carlson","orcid":null,"position":3,"is_corresponding":false},{"id":1273158,"name":"Ming Dong","orcid":"0000-0001-8133-7809","position":4,"is_corresponding":false},{"id":671573,"name":"Robert Rothermel","orcid":null,"position":5,"is_corresponding":false},{"id":449609,"name":"Csaba Juhász","orcid":"0000-0002-5067-5554","position":6,"is_corresponding":false},{"id":311335,"name":"Eishi Asano","orcid":"0000-0001-8391-4067","position":7,"is_corresponding":false},{"id":1265491,"name":"Jeong-Won Jeong","orcid":null,"position":8,"is_corresponding":false},{"id":311338,"name":"Min‐Hee Lee","orcid":"0000-0002-1932-9557","position":0,"is_corresponding":true}],"reference_count":64,"raw_metadata":null,"created_at":"2026-07-19T02:02:45.951080Z","pmid":"39292577","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}