AIMC Topic: Brain Injuries, Traumatic

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Response to Wang et al., (doi: 10.1089/neu.2019.6705).

Journal of neurotrauma Dec 20, 2023
Brain Injuries, Traumatic Unsupervised Machine Learning Humans Phenotype
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Prediction of therapeutic intensity level from automatic multiclass segmentation of traumatic brain injury lesions on CT-scans.

Scientific reports Nov 17, 2023
The prediction of the therapeutic intensity level (TIL) for severe traumatic brain injury (TBI) patients at the early phase of intensive care unit (ICU) remains challenging. Computed tomography images are still manually quantified and then underexplo...
Brain Injuries, Traumatic Head Tomography, X-Ray Computed Image Processing, Computer-Assisted Humans Neural Networks, Computer Artificial Intelligence
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Response to Folweiler KA et al., Unsupervised Machine Learning Reveals Novel Traumatic Brain Injury Patient Phenotypes With Distinct Acute Injury Profiles and Long-Term Outcomes (DOI: 10.1089/neu.2019.6705).

Journal of neurotrauma Oct 18, 2023
Unsupervised Machine Learning Phenotype Brain Injuries, Traumatic Humans
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Mortality prediction using medical time series on TBI patients.

Computer methods and programs in biomedicine Sep 21, 2023
BACKGROUND AND OBJECTIVE: Traumatic Brain Injury (TBI) is one of the leading causes of injury-related mortality in the world, with severe cases reaching mortality rates of 30-40%. It is highly heterogeneous both in causes and consequences making more...
Humans Time Factors Brain Injuries, Traumatic Artificial Intelligence Prognosis Critical Care
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A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage.

Scientific reports Jun 20, 2023
Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) requires prompt radiological investigation and recognition by physicians. Computed tomography (CT) scanning is the investigation of choice for TBI and has become increasingly utilized un...
Neurosurgery Intracranial Hemorrhages Brain Injuries, Traumatic Humans Tomography, X-Ray Computed Deep Learning
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Validation of a deep learning model for traumatic brain injury detection and NIRIS grading on non-contrast CT: a multi-reader study with promising results and opportunities for improvement.

Neuroradiology Jun 3, 2023
PURPOSE: This study aimed to assess and externally validate the performance of a deep learning (DL) model for the interpretation of non-contrast computed tomography (NCCT) scans of patients with suspicion of traumatic brain injury (TBI).
Neuroimaging Brain Injuries, Traumatic Hydrocephalus Humans Retrospective Studies Tomography, X-Ray Computed Deep Learning
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An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 29, 2023
A longstanding challenge surrounding deep learning algorithms is unpacking and understanding how they make their decisions. Explainable Artificial Intelligence (XAI) offers methods to provide explanations of internal functions of algorithms and reaso...
Time Factors Artificial Intelligence Humans Brain Injuries, Traumatic Algorithms Research Personnel
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Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics.

Journal of sport and health science Mar 13, 2023
BACKGROUND: Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo, and the characteristics of different types of impacts are...
Humans Mouth Protectors Brain Injuries, Traumatic Machine Learning Head Biomechanical Phenomena
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An end-end deep learning framework for lesion segmentation on multi-contrast MR images-an exploratory study in a rat model of traumatic brain injury.

Medical & biological engineering & computing Jan 10, 2023
Traumatic brain injury (TBI) engenders traumatic necrosis and penumbra-areas of secondary neural injury which are crucial targets for therapeutic interventions. Segmenting manually areas of ongoing changes like necrosis, edema, hematoma, and inflamma...
Magnetic Resonance Imaging Brain Injuries, Traumatic Rats, Sprague-Dawley Image Processing, Computer-Assisted Animals Rats Deep Learning Cohort Studies
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A Machine Learning-Based Approach to Predict Prognosis and Length of Hospital Stay in Adults and Children With Traumatic Brain Injury: Retrospective Cohort Study.

Journal of medical Internet research Dec 9, 2022
BACKGROUND: The treatment and care of adults and children with traumatic brain injury (TBI) constitute an intractable global health problem. Predicting the prognosis and length of hospital stay of patients with TBI may improve therapeutic effects and...
Retrospective Studies Adult Neural Networks, Computer Machine Learning Brain Injuries, Traumatic Length of Stay Humans Child
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