ArticleDiagnostics (Basel, Switzerland)2022
Detection of Chronic Blast-Related Mild Traumatic Brain Injury with Diffusion Tensor Imaging and Support Vector Machines.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- Application of Machine Learning in the Diagnosis and Prognosis of Mild Traumatic Brain Injury Using Diffusion Tensor Imaging: A Systematic Review.Journal of magnetic resonance imaging : JMRI · 2026Pooled it
- The second decade of DTI in TBI part 1: a systematic review of mild TBI.Frontiers in neurologyPooled it
- Forensic neuroimaging in childhood abduction, rape and murder case and trial outcome.Psychiatry, psychology, and law : an interdisciplinary journal of the Australian and New Zealand Association of Psychiatry, Psychology and Law · 2026Article
- Assessment model of blast injury: A narrative review.iScience · 2025Review
- Resting-state EEG and machine learning to investigate cortical connectivity as a biomarker in chronic mTBI.Frontiers in neurology · 2025Article
- Lifetime Blast Exposure Is Not Related to White Matter Integrity in Service Members and Veterans With and Without Uncomplicated Mild Traumatic Brain Injury.Neurotrauma reports · 2023Article
- Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques.Frontiers in neuroscience · 2022Article
- Combining Multiple Indices of Diffusion Tensor Imaging Can Better Differentiate Patients with Traumatic Brain Injury from Healthy Subjects.Neuropsychiatric disease and treatment · 2022Article
Corrections and comments
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Authors and funding
18 authors at 5 institutions in 2 countries.
Funding
Abstract
Blast-related mild traumatic brain injury (bmTBI) often leads to long-term sequalae, but diagnostic approaches are lacking due to insufficient knowledge about the predominant pathophysiology. This study aimed to build a diagnostic model for future verification by applying machine-learning based support vector machine (SVM) modeling to diffusion tensor imaging (DTI) datasets to elucidate white-matter features that distinguish bmTBI from healthy controls (HC). Twenty subacute/chronic bmTBI and 19 HC combat-deployed personnel underwent DTI. Clinically relevant features for modeling were selected using tract-based analyses that identified group differences throughout white-matter tracts in five DTI metrics to elucidate the pathogenesis of injury. These features were then analyzed using SVM modeling with cross validation. Tract-based analyses revealed abnormally decreased radial diffusivity (RD), increased fractional anisotropy (FA) and axial/radial diffusivity ratio (AD/RD) in the bmTBI group, mostly in anterior tracts (29 features). SVM models showed that FA of the anterior/superior corona radiata and AD/RD of the corpus callosum and anterior limbs of the internal capsule (5 features) best distinguished bmTBI from HCs with 89% accuracy. This is the first application of SVM to identify prominent features of bmTBI solely based on DTI metrics in well-defined tracts, which if successfully validated could promote targeted treatment interventions.
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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.