Neurology

Head Trauma

Latest AI and machine learning research in head trauma for healthcare professionals.

6,666 articles
Stay Ahead - Weekly Head Trauma research updates
Subscribe
Browse Specialties
Showing 1-21 of 6,666 articles
Improvements from incorporating machine learning algorithms into near real-time operational post-processing.

During regional seismic monitoring, data is automatically analyzed in real-time to identify events a...

Integrated 3D Modeling and Functional Simulation of the Human Amygdala: A Novel Anatomical and Computational Analyses.

The amygdala plays a central role in emotion, memory, and decision-making and comprises approximatel...

Predicting post-traumatic stress disorder in relatives of critically ill patients.

PURPOSE OF REVIEW: Symptoms of posttraumatic stress disorder (PTSD) affect up to a third of relative...

Deep learning-based radiomics does not improve residual cancer burden prediction post-chemotherapy in LIMA breast MRI trial.

OBJECTIVES: This study aimed to evaluate the potential additional value of deep radiomics for assess...

Development and interpretation of a machine learning risk prediction model for post-stroke depression in a Chinese population.

Current evidence for predictive models of post-stroke depression (PSD) risk based on machine learnin...

NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search.

Deep neural network (DNN) deployment has been confined to larger hardware devices due to their expen...

Performance of Clinical Risk Prediction Models for Post-ERCP Pancreatitis: A Systematic Review.

OBJECTIVES: Pancreatitis is common following endoscopic retrograde cholangiopancreatography (ERCP). ...

β-lactam resistance: epidemiological trends, molecular drivers, and innovative control strategies in the post-pandemic era.

SUMMARY () is a major human pathogen that can cause severe diseases such as meningitis and bacteremi...

Evaluating the impact of an AI-powered chatbot on epilepsy education and stigma reduction: A pre-post intervention study using EpiloBot.

OBJECTIVE: Effective epilepsy management requires accurate epilepsy knowledge, active patient engage...

Connectomic stroke lesion measures provide no benefit over basic spatial lesion features in the prognosis of global stroke outcome measures.

The prediction of stroke outcome from imaging markers could be used to guide individualized therapeu...

Identifying key physiological and clinical factors for traumatic brain injury patient management using network analysis and machine learning.

In the intensive care unit (ICU), managing traumatic brain injury (TBI) patients presents significan...

Quantification of hepatic steatosis on post-contrast computed tomography scans using artificial intelligence tools.

PURPOSE: Early detection of steatotic liver disease (SLD) is critically important. In clinical pract...

Predicting outcomes following endovascular aortoiliac revascularization using machine learning.

Endovascular aortoiliac revascularization is a common treatment option for peripheral artery disease...

Predicting Traumatic Brain Injury Post-Trauma Using Temporal Attention on Sleep-Wake Data.

BACKGROUND: Traumatic Brain Injury (TBI) is a major public health concern, and accurate classificati...

Post-stroke aphasia analysis using topological alterations in brain functional networks.

. Nearly one-third of stroke patients develop aphasia. Although the function of classical language a...

Ultra-low dose imaging in a standard axial field-of-view PET.

Though ultra-low dose (ULD) imaging offers notable benefits, its widespread clinical adoption faces ...

Emulating real-world GLP-1 efficacy in type 2 diabetes through causal learning and virtual patients.

Randomized controlled trials (RCTs) remain the benchmark for assessing treatment effects but are lim...

Browse Specialties