Evidence map›Paper›PMID 41638061›Full record

ArticleMedical image analysis2026

ESM-AnatTractNet: Advanced deep learning model of true positive eloquent white matter tractography to improve preoperative evaluation of pediatric epilepsy surgery.

Min-Hee Lee, Bohan Xiao, Soumyanil Banerjee, Hiroshi Uda, Yoon Ho Hwang, Csaba Juhász, Eishi Asano, Ming Dong, Jeong-Won Jeong

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Article in Medical image analysis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Min-Hee LeeDepartment of Pediatrics, Wayne State University, Detroit, 48201, MI, USA; Translational Imaging Laboratory, University Health Center, Detroit, 48201, MI, USA. Electronic address: min-hee.lee@wayne.edu.
Bohan XiaoDepartment of Computer Science, Wayne State University, Detroit, 48201, MI, USA. Electronic address: gk6511@wayne.edu.
Soumyanil BanerjeeDepartment of Computer Science, Wayne State University, Detroit, 48201, MI, USA. Electronic address: s.banerjee@wayne.edu.
Hiroshi UdaDepartment of Pediatrics, Wayne State University, Detroit, 48201, MI, USA. Electronic address: hm9480@wayne.edu.
Yoon Ho HwangDepartment of Pediatrics, Wayne State University, Detroit, 48201, MI, USA; Translational Imaging Laboratory, University Health Center, Detroit, 48201, MI, USA. Electronic address: ia7758@wayne.edu.
Csaba JuhászDepartment of Pediatrics, Wayne State University, Detroit, 48201, MI, USA; Translational Imaging Laboratory, University Health Center, Detroit, 48201, MI, USA; Translational Neuroscience Program, Wayne State University, Detroit, 48201, MI, USA; Department of Neurology, Wayne State University, Detroit, 48201, MI, USA. Electronic address: csaba.juhasz@wayne.edu.
Eishi AsanoDepartment of Pediatrics, Wayne State University, Detroit, 48201, MI, USA; Translational Imaging Laboratory, University Health Center, Detroit, 48201, MI, USA; Translational Neuroscience Program, Wayne State University, Detroit, 48201, MI, USA; Department of Neurology, Wayne State University, Detroit, 48201, MI, USA. Electronic address: easano@med.wayne.edu.
Ming DongDepartment of Computer Science, Wayne State University, Detroit, 48201, MI, USA. Electronic address: mdong@wayne.edu.
Jeong-Won JeongDepartment of Pediatrics, Wayne State University, Detroit, 48201, MI, USA; Translational Imaging Laboratory, University Health Center, Detroit, 48201, MI, USA; Translational Neuroscience Program, Wayne State University, Detroit, 48201, MI, USA; Department of Neurology, Wayne State University, Detroit, 48201, MI, USA. Electronic address: jjeong@med.wayne.edu.

Funding

Functional brain mapping in pediatric neurosurgeryR01NS064033 · NINDS · WAYNE STATE UNIVERSITY · PI EISHI ASANO · 2009 to 2026
$5.7M
Novel DWI Methods to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery.R01NS089659 · NINDS · WAYNE STATE UNIVERSITY · PI JEONG, JEONG-WON · 2015 to 2025
$3.7M
NINDS NIH HHS R01 NS064033NINDS NIH HHS R01 NS089659
6 · The paper itself

Abstract

Accurate preoperative identification of true positive white matter pathways involved in critical eloquent functions such as motor, language, and vision plays a vital role in minimizing the risk of postoperative functional deficits and improving postoperative functional outcomes in pediatric epilepsy surgery. This study proposes a novel deep learning model: "ESM-AnatTractNet" that can accurately classify true positive eloquent white matter pathways across preoperative diffusion weighted imaging tractography data of 85 drug-resistant epilepsy patients (age: 10.70 ± 4.41 years). To enhance geometric and anatomical consistency of true positive tract classification, the ESM-AnatTractNet integrated two features in a point-cloud-based framework, 1) electro-physiologically confirmed spatial coordinates using electrical stimulation mapping (ESM) and 2) anatomically-contexted labels of the end-to-end neural connection using a standard brain atlas. Its overall performance was validated by accurately classifying 14 eloquent functional areas in whole brain, objectively optimizing resection margins to preserve eloquent functions using Kalman filter, and precisely predicting postoperative language outcomes using canonical correlation. Our ESM-AnatTractNet outperformed other baseline models, achieving an accuracy of 97% in correctly classifying eloquent areas within 10mm spatial resolution of clinical subdural grid electroencephalography. The Kalman filter analysis achieved 94% accuracy in predicting no deficits when the ESM-AnatTractNet-defined preservation zones were not resected. Postoperative decrease in language-related white matter connection efficacy defined by the ESM-AnatTractNet analysis was significantly associated with worse postoperative language outcome (R=0.73, p < 0.001). Our findings demonstrate that the ESM-AnatTractNet improves non-invasive localization of true positive eloquent white matter pathways, supporting its potential to enhance current preoperative evaluation of pediatric epilepsy surgery.

Indexed as

Deep LearningDiffusion Tensor ImagingDrug Resistant EpilepsyEpilepsyWhite MatterAdolescentBrain MappingChildFemaleHumansMalePreoperative CareDeep learning modelDiffusion weighted imaging tractographyElectrical stimulation mappingPediatric epilepsy surgeryPreoperative evaluation

Identifiers

PMID41638061
PMCPMC12905673

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.