ArticleIEEE transactions on medical imaging2019
Objective Detection of Eloquent Axonal Pathways to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery using Diffusion Tractography and Convolutional Neural Networks.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 50 citations in OpenAlex.
- ESM-AnatTractNet: Advanced deep learning model of true positive eloquent white matter tractography to improve preoperative evaluation of pediatric epilepsy surgery.Medical image analysis · 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
- Comprehensive review of machine learning and deep learning techniques for epileptic seizure detection and prediction based on neuroimaging modalities.Visual computing for industry, biomedicine, and art · 2025Review
- Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy.IEEE transactions on bio-medical engineering · 2025Article
- Tractography-Based Automated Identification of Retinogeniculate Visual Pathway With Novel Microstructure-Informed Supervised Contrastive Learning.Human brain mapping · 2024Article
- Quantitative phenotyping of verbal and non-verbal cognitive impairment using diffusion-weighted MRI connectome: Preliminary study of the crowding effect in children with left hemispheric epilepsy.Epilepsy & behavior : E&B · 2024Article
- Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation.NeuroImage · 2023Article
- Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- [A research on epilepsy source localization from scalp electroencephalograph based on patient-specific head model and multi-dipole model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Superficial white matter analysis: An efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dMRI acquisitions.Medical image analysis · 2023Article
- Computational approaches for the reconstruction of optic nerve fibers along the visual pathway from medical images: a comprehensive review.Frontiers in neuroscience · 2023Review
- Multi-Scale Deep Learning of Clinically Acquired Multi-Modal MRI Improves the Localization of Seizure Onset Zone in Children With Drug-Resistant Epilepsy.IEEE journal of biomedical and health informatics · 2022Article
- Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI.IEEE transactions on neural networks and learning systems · 2022Article
- Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review.NeuroImage · 2022Review
- Prediction of baseline expressive and receptive language function in children with focal epilepsy using diffusion tractography-based deep learning network.Epilepsy & behavior : E&B · 2021Article
- Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children.Frontiers in computational neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Convolutional neural networks (CNNs) have recently been used in biomedical imaging applications with great success. In this paper, we investigated the classi?cation performance of CNN models on diffusion weighted imaging (DWI) streamlines de?ned by functional MRI (fMRI) and electrical stimulation mapping (ESM). To learn a set of discriminative and interpretable features from the extremely unbalanced dataset, we evaluated different CNN architectures with multiple loss functions (e.g., focal loss and center loss) and a soft attention mechanism, and compared our models with current state-ofthe-art methods. Through extensive experiments on streamlines collected from 70 healthy children and 70 children with focal epilepsy, we demonstrated that our deep CNN model with focal and central losses and soft attention outperforms all existing models in the literature and provides clinically acceptable accuracy (73 -100%) for the objective detection of functionally-important white matter pathways including ESM determined eloquent areas such as primary motor, aphasia, speech arrest, auditory, and visual functions. The ?ndings of this study encourage further investigations to determine if DWICNN analysis can serve as a noninvasive diagnostic tool during pediatric presurgical planning by estimating not only the location of essential cortices at the gyral level, but also the underlying ?bers connecting these cortical areas, to minimize or predict postsurgical functional de?cits. This study translates an advanced CNN model to clinical practice in the pediatric population where currently available approaches (e.g., ESM, fMRI) are suboptimal. The implementation will be released at https://github. com/HaotianMXu/Brain-?ber-classi?cation-using-CNNs.
Identifiers
What OpenQuestion holds
Registered trials
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.