Head Trauma

Machine learning-based identification of the risk factors for postoperative nausea and vomiting in adults.

Postoperative nausea and vomiting (PONV) is a common adverse effect of anesthesia. Identifying risk factors for PONV is crucial because it is associated with a longer stay in the post-anesthesia care unit, readmissions, and perioperative costs. This retrospective study used artificial intelligence to analyze data of 37,548 adult patients (aged ≥20 years) who underwent surgery under general anesthe...

Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter.

The COVID-19 pandemic has disrupted people's lives and caused significant economic damage around the world, but its impact on people's mental health has not been paid due attention by the research community. According to anecdotal data, the pandemic has raised serious concerns related to mental health among the masses. However, no systematic investigations have been conducted previously on mental ...

Predictive Models of Long-Term Outcome in Patients with Moderate to Severe Traumatic Brain Injury are Biased Toward Mortality Prediction.

BACKGROUND: The prognostication of long-term functional outcomes remains challenging in patients with traumatic brain injury (TBI). Our aim was to dem...

Predicting the trajectory of non-suicidal self-injury among adolescents.

BACKGROUND: Non-suicidal self-injury (NSSI) is common among adolescents receiving inpatient psychiatric treatment and the months post-discharge is a h...

Accuracy and time efficiency of a novel deep learning algorithm for Intracranial Hemorrhage detection in CT Scans.

PURPOSE: To evaluate a deep learning-based pipeline using a Dense-UNet architecture for the assessment of acute intracranial hemorrhage (ICH) on non-c...

Exploring driving behavioral characteristics in pre-, in-, and post-conflict stages based on car-following trajectory data.

This study investigates driving behaviour in different stages of rear-end conflicts using vehicle trajectory data. Three conflict stages (pre-, in-, a...

Evaluating performance of a near-infrared-spectroscopy-based and machine-learning-powered bio-optical sensitivity parameters in identifying intracranial hemorrhages in TBI across different age-groups.

PRIMARY OBJECTIVE: To evaluate the accuracy of an innovative machine-learning-powered near-infrared spectroscopy (mNIRS)-based bio-optical sensitivity...

MEFFNet: Forecasting Myoelectric Indices of Muscle Fatigue in Healthy and Post-Stroke During Voluntary and FES-Induced Dynamic Contractions.

Myoelectric indices forecasting is important for muscle fatigue monitoring in wearable technologies, adaptive control of assistive devices like exoske...

Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review.

BACKGROUND: This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease...

Machine learning for automating subjective clinical assessment of gait impairment in people with acquired brain injury - a comparison of an image extraction and classification system to expert scoring.

BACKGROUND: Walking impairment is a common disability post acquired brain injury (ABI), with visually evident arm movement abnormality identified as n...

EfficientQ: An efficient and accurate post-training neural network quantization method for medical image segmentation.

Model quantization is a promising technique that can simultaneously compress and accelerate a deep neural network by limiting its computation bit-widt...

AI-enabled ECG index for predicting left ventricular dysfunction in patients with ST-segment elevation myocardial infarction.

Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients ...

Robot-related injuries in the workplace: An analysis of OSHA Severe Injury Reports.

Industrial robots are increasingly commonplace, but research on prototypical accidents and injuries has been sparse, hindering evidence-based safety s...

Speech-based recognition and estimating severity of PTSD using machine learning.

BACKGROUND: Traditional methodologies for diagnosing post-traumatic stress disorder (PTSD) primarily rely on interviews, incurring considerable costs ...

Improving diagnostic confidence in low-dose dual-energy CTE with low energy level and deep learning reconstruction.

OBJECTIVE: To demonstrate the value of using 50 keV virtual monochromatic images with deep learning image reconstruction (DLIR) in low-dose dual-energ...

Machine Learning Based Prediction of Post-operative Infrarenal Endograft Apposition for Abdominal Aortic Aneurysms.

OBJECTIVE: Challenging infrarenal aortic neck characteristics have been associated with an increased risk of type Ia endoleak after endovascular aneur...

Machine learning methods in automated detection of CT enterography findings in Crohn's disease: A feasibility study.

PURPOSE: Qualitative findings in Crohn's disease (CD) can be challenging to reliably report and quantify. We evaluated machine learning methodologies ...

A novel virtual robotic platform for controlling six degrees of freedom assistive devices with body-machine interfaces.

Body-machine interfaces (BoMIs)-systems that control assistive devices (e.g., a robotic manipulator) with a person's movements-offer a robust and non-...

Enhancing post-training evaluation of annual performance agreement training: A fusion of fsQCA and artificial neural network approach.

This study aims to enhance the post-training evaluation of the annual performance agreement (APA) training organized by the Bangladesh Public Administ...

Deep learning-based automated detection and segmentation of bone and traumatic bone marrow lesions from MRI following an acute ACL tear.

INTRODUCTION: Traumatic bone marrow lesions (BML) are frequently identified on knee MRI scans in patients following an acute full-thickness, complete ...