Head Trauma

Multivariate modeling and prediction of cerebral physiology in acute traumatic neural injury: A scoping review.

Traumatic brain injury (TBI) poses a significant global public health challenge necessitating a profound understanding of cerebral physiology. The dynamic nature of TBI demands sophisticated methodologies for modeling and predicting cerebral signals to unravel intricate pathophysiology and predict secondary injury mechanisms prior to their occurrence. In this comprehensive scoping review, we focus...

Prediction of post-delivery hemoglobin levels with machine learning algorithms.

Predicting postpartum hemorrhage (PPH) before delivery is crucial for enhancing patient outcomes, enabling timely transfer and implementation of prophylactic therapies. We attempted to utilize machine learning (ML) using basic pre-labor clinical data and laboratory measurements to predict postpartum Hemoglobin (Hb) in non-complicated singleton pregnancies. The local databases of two academic care ...

Quantifying social capital creation in post-disaster recovery aid in Indonesia: methodological innovation by an AI-based language model.

Smooth interaction with a disaster-affected community can create and strengthen its social capital, leading to greater effectiveness in the provision ...

Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records.

BACKGROUND: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial ...

Can machine learning predict late seizures after intracerebral hemorrhages? Evidence from real-world data.

INTRODUCTION: Intracerebral hemorrhage represents 15 % of all strokes and it is associated with a high risk of post-stroke epilepsy. However, there ar...

Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning.

Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigat...

Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans.

In this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-...

Machine learning models for predicting early hemorrhage progression in traumatic brain injury.

This study explores the progression of intracerebral hemorrhage (ICH) in patients with mild to moderate traumatic brain injury (TBI). It aims to predi...

Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types.

OBJECTIVE: The machine-learning head model (MLHM) to accelerate the calculation of brain strain and strain rate, which are the predictors for traumati...

A complex fuzzy decision model for analysing the post-pandemic immuno-sustainability.

The post-effects of the COronaVIrus Disease (COVID-19) vary depending on socioeconomic and biological factors. Similarly, the effects of vaccination o...

Usefulness of Artificial Intelligence in Traumatic Brain Injury: A Bibliometric Analysis and Mini-review.

BACKGROUND: Traumatic brain injury (TBI) has become a major source of disability worldwide, increasing the interest in algorithms that use artificial ...

Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment.

This study investigates the application of cavitation in non-invasive abdominal fat reduction and body contouring, a topic of considerable interest in...

The Impact of Drop Test Conditions on Brain Strain Location and Severity: A Novel Approach Using a Deep Learning Model.

In contact sports such as rugby, players are at risk of sustaining traumatic brain injuries (TBI) due to high-intensity head impacts that generate hig...

Accuracy of early pregnancy diagnosis and determining pregnancy loss using different biomarkers and machine learning applications in dairy cattle.

This study aimed to compare the accuracy of IFN-τ stimulated gene abundance (ISGs) in peripheral blood mononuclear cells (PBMCs), CL blood perfusion b...

Using machine learning to increase access to and engagement with trauma-focused interventions for posttraumatic stress disorder.

BACKGROUND: Post-traumatic stress disorder (PTSD) poses a global public health challenge. Evidence-based psychotherapies (EBPs) for PTSD reduce sympto...

Machine learning for predicting elective fertility preservation outcomes.

This retrospective study applied machine-learning models to predict treatment outcomes of women undergoing elective fertility preservation. Two-hundre...

Exploring post-COVID-19 health effects and features with advanced machine learning techniques.

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health...

Artificial Intelligence for Quantifying Cumulative Small Bowel Disease Severity on CT-Enterography in Crohn's Disease.

INTRODUCTION: Assessing the cumulative degree of bowel injury in ileal Crohn's disease (CD) is difficult. We aimed to develop machine learning (ML) me...

Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study.

BACKGROUND: Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appear...

The gut microbiome associates with phenotypic manifestations of post-acute COVID-19 syndrome.

The mechanisms underlying the many phenotypic manifestations of post-acute COVID-19 syndrome (PACS) are poorly understood. Herein, we characterized th...