AIMC Topic: Brain Injuries, Traumatic
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Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks.
Brain and cognition
Mar 26, 2018
The accurate diagnosis and assessment of neurodegenerative disease and traumatic brain injuries (TBI) remain open challenges. Both cause cognitive and functional deficits due to focal axonal swellings (FAS), but it is difficult to deliver a prognosis...
Therapeutic hypothermia in patients with coagulopathy following severe traumatic brain injury.
Scandinavian journal of trauma, resuscitation and emergency medicine
Dec 20, 2017
BACKGROUND: Coagulopathy in traumatic brain injury (TBI) has been associated with poor neurological outcomes and higher in-hospital mortality. In general principle of trauma management, hypothermia should be prevented as it directly worsens coagulopa...
Robotics in Biomedical and Healthcare Engineering.
Journal of healthcare engineering
Aug 28, 2017
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation.
Medical image analysis
Oct 29, 2016
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks pro...
A machine learning approach to identify functional biomarkers in human prefrontal cortex for individuals with traumatic brain injury using functional near-infrared spectroscopy.
Brain and behavior
Aug 24, 2016
BACKGROUND: We have explored the potential prefrontal hemodynamic biomarkers to characterize subjects with Traumatic Brain Injury (TBI) by employing the multivariate machine learning approach and introducing a novel task-related hemodynamic response ...
Identifying Important Attributes for Prognostic Prediction in Traumatic Brain Injury Patients. A Hybrid Method of Decision Tree and Neural Network.
Methods of information in medicine
Aug 5, 2016
BACKGROUND: Generally, traumatic brain injury (TBI) patients do not have a stable condition, particularly after the first week of TBI. Hence, indicating the attributes in prognosis through a prediction model is of utmost importance since it helps car...
Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks.
NeuroImage
Jan 28, 2016
Identifying diffuse axonal injury (DAI) in patients with traumatic brain injury (TBI) presenting with normal appearing radiological MRI presents a significant challenge. Neuroimaging methods such as diffusion MRI and probabilistic tractography, which...
Optimizing Vital Signs in Patients With Traumatic Brain Injury: Reinforcement Learning Algorithm Development and Validation.
Journal of medical Internet research
Jul 3, 2025
BACKGROUND: Traumatic brain injury (TBI) is a critically ill disease with a high mortality rate, and clinical treatment is committed to continuously optimizing treatment strategies to improve survival rates.
Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.
Scientific reports
Jul 2, 2025
To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing early intracranial hypertension (IH) following primary decompressive craniectomy (DC) in patients with severe traumatic brain injury (TBI) and to demonstrate its pote...
Linking Symptom Inventories Using Semantic Textual Similarity.
Journal of neurotrauma
Jun 1, 2025
An extensive library of symptom inventories has been developed over time to measure clinical symptoms of traumatic brain injury (TBI), but this variety has led to several long-standing issues. Most notably, results drawn from different settings and s...
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