AIMC Topic: Brain Injuries
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Feasibility of Overground Gait Training Using a Joint-Torque-Assisting Wearable Exoskeletal Robot in Children with Static Brain Injury.
Sensors (Basel, Switzerland)
May 19, 2022
Pediatric gait disorders are often chronic and accompanied by various complications, which challenge rehabilitation efforts. Here, we retrospectively analyzed the feasibility of overground robot-assisted gait training (RAGT) using a joint-torque-assi...
Deep learning methodology for predicting time history of head angular kinematics from simulated crash videos.
Scientific reports
Apr 20, 2022
Head kinematics information is important as it is used to measure brain injury risk. Currently, head kinematics are measured using wearable devices or instrumentation mounted on the head. This paper evaluates the deep learning approach in predicting ...
Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning.
Nature communications
Feb 25, 2022
Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an ex...
Integrated Multiomics Analysis Identifies a Novel Biomarker Associated with Prognosis in Intracerebral Hemorrhage.
Oxidative medicine and cellular longevity
Dec 14, 2021
Existing treatments for intracerebral hemorrhage (ICH) are unable to satisfactorily prevent development of secondary brain injury after ICH and multiple pathological mechanisms are involved in the development of the injury. In this study, we aimed to...
Application of a deep learning image reconstruction (DLIR) algorithm in head CT imaging for children to improve image quality and lesion detection.
BMC medical imaging
Jul 8, 2021
BACKGROUND: To evaluate the performance of a Deep Learning Image Reconstruction (DLIR) algorithm in pediatric head CT for improving image quality and lesion detection with 0.625 mm thin-slice images.
A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact.
Medical & biological engineering & computing
Sep 21, 2020
Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details co...
Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma.
BMC cancer
Jun 1, 2020
BACKGROUND: Early radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction models of RTLI are lacking. Hence, we aimed to develop radiomic models for early detection of RTLI.
Temporal Lobe
Radiation Injuries
Nasopharyngeal Carcinoma
Nasopharyngeal Neoplasms
Humans
Machine Learning
Longitudinal Studies
Magnetic Resonance Imaging
Brain Injuries
Predictive Value of Tests
Adult
Aftercare
Models, Biological
Algorithms
Male
Image Interpretation, Computer-Assisted
Retrospective Studies
Middle Aged
Female
ROC Curve
Machine learning modelling of neuropsychological performance could determine vocational training outcome after a brain injury: Case report.
Annals of physical and rehabilitation medicine
Apr 11, 2020
Effect of Robot-Assisted Gait Training in a Large Population of Children With Motor Impairment Due to Cerebral Palsy or Acquired Brain Injury.
Archives of physical medicine and rehabilitation
Sep 25, 2019
OBJECTIVE: To evaluate retrospectively the effect of robotic rehabilitation in a large group of children with motor impairment; an additional goal was to identify the effects in children with cerebral palsy (CP) and acquired brain injury (ABI) and wi...
Modelling outcomes after paediatric brain injury with admission laboratory values: a machine-learning approach.
Pediatric research
Jul 26, 2019
BACKGROUND: Severe traumatic brain injury (TBI) is a leading cause of mortality in children, but the accurate prediction of outcomes at the point of admission remains very challenging. Admission laboratory results are a promising potential source of ...
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