Head Trauma

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 assessme...

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 tr...

Optimizing post-transplantation outcomes: the role of multi-omics, artificial intelligence, and animal models in addressing immunosuppression-associated hepatotoxicity.

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 ...

Prediction of post-donation renal function using machine learning techniques and conventional regression models in living kidney donors.

BACKGROUND: Accurate prediction of renal function following kidney donation and careful selection of...

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,...

Combining Metabolomics and Machine Learning to Identify Diagnostic and Prognostic Biomarkers in Patients with Non-Small Cell Lung Cancer Pre- and Post-Radiation Therapy.

Lung cancer is the leading cause of cancer-related deaths globally, with non-small cell lung cancer ...

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...

Machine Learning Predicts Unplanned Care Escalations for Post-Anesthesia Care Unit Patients during the Perioperative Period: A Single-Center Retrospective Study.

BACKGROUND:  Despite low mortality for elective procedures in the United States and developed countr...

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 visuall...

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 n...

Predicting Post-surgery Discharge Time in Pediatric Patients Using Machine Learning.

BACKGROUND: Prolonged hospital stays after pediatric surgeries, such as tonsillectomy and adenoidect...

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...

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 ...

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

BACKGROUND: Traditional methodologies for diagnosing post-traumatic stress disorder (PTSD) primarily...

Regularized ensemble learning for prediction and risk factors assessment of students at risk in the post-COVID era.

The COVID-19 pandemic has had a significant impact on students' academic performance. The effects of...

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 ...

Clinician perceptions of a novel wearable robotic hand orthosis for post-stroke hemiparesis.

PURPOSE: Wearable robotic devices are currently being developed to improve upper limb function for i...

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...

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 quan...

A comparative analysis of deep learning and hybrid iterative reconstruction algorithms with contrast-enhancement-boost post-processing on the image quality of indirect computed tomography venography of the lower extremities.

PURPOSE: To examine whether there is a significant difference in image quality between the deep lear...