Domestic Violence

Computed Tomography of the Spine : Systematic Review on Acquisition and Reconstruction Techniques to Reduce Radiation Dose.

The introduction of the first whole-body CT scanner in 1974 marked the beginning of cross-sectional spine imaging. In the last decades, the technological advancement, increasing availability and clinical success of CT led to a rapidly growing number of CT examinations, also of the spine. After initially being primarily used for trauma evaluation, new indications continued to emerge, such as assess...

Robust deep learning-based forward dose calculations for VMAT on the 1.5T MR-linac.

In this work we present a framework for robust deep learning-based VMAT forward dose calculations for the 1.5T MR-linac. A convolutional neural network was trained on the dose of individual multi-leaf-collimator VMAT segments and was used to predict the dose per segment for a set of MR-linac-deliverable VMAT test plans. The training set consisted of prostate, rectal, lung and esophageal tumour dat...

Retrospective analysis and prospective validation of an AI-based software for intracranial haemorrhage detection at a high-volume trauma centre.

Rapid detection of intracranial haemorrhage (ICH) is crucial for assessing patients with neurological symptoms. Prioritising these urgent scans for re...

A Pilot Machine Learning Study Using Trauma Admission Data to Identify Risk for High Length of Stay.

INTRODUCTION: Trauma patients have diverse resource needs due to variable mechanisms and injury patterns. The aim of this study was to build a tool th...

Deffini: A family-specific deep neural network model for structure-based virtual screening.

Deep learning-based virtual screening methods have been shown to significantly improve the accuracy of traditional docking-based virtual screening met...

Key factors selection on adolescents with non-suicidal self-injury: A support vector machine based approach.

Comparing a family structure to a company, one can often think of parents as leaders and adolescents as employees. Stressful family environments and a...

Methods for Clinical Evaluation of Artificial Intelligence Algorithms for Medical Diagnosis.

Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in practice is critical. Clinical evaluation aims to confirm a...

A machine learning method for predicting the probability of MODS using only non-invasive parameters.

OBJECTIVES: Timely and accurate prediction of multiple organ dysfunction syndrome (MODS) is essential for the rescue and treatment of trauma patients ...

Microfluidics guided by deep learning for cancer immunotherapy screening.

Immunocyte infiltration and cytotoxicity play critical roles in both inflammation and immunotherapy. However, current cancer immunotherapy screening m...

Intelligent injury prediction for traumatic airway obstruction.

Airway obstruction is one of the crucial causes of death in trauma patients during the first aid. It is extremely challenging to accurately treat a gr...

A Natural Language Processing and Machine Learning Approach to Identification of Incidental Radiology Findings in Trauma Patients Discharged from the Emergency Department.

STUDY OBJECTIVE: Patients undergoing diagnostic imaging studies in the emergency department (ED) commonly have incidental findings, which may represen...

Progress in Control-Actuation Robotic System for Gastrointestinal NOTES Development.

PURPOSE: Natural orifice transluminal endoscopic surgery (NOTES) is a minimally invasive surgical procedure that reduces patient trauma, infection pro...

Deep Learning Hybrid Techniques for Brain Tumor Segmentation.

Medical images play an important role in medical diagnosis and treatment. Oncologists analyze images to determine the different characteristics of dea...

Clinical safety and efficacy of a fully automated robot for magnetic resonance imaging-guided breast biopsy.

BACKGROUND: Magnetic resonance imaging (MRI)-guided biopsies are an accurate, but technically challenging, method for screening and diagnosis of breas...

Brain Age Prediction/Classification through Recurrent Deep Learning with Electroencephalogram Recordings of Seizure Subjects.

With modern population growth and an increase in the average lifespan, more patients are becoming afflicted with neurodegenerative diseases such as de...

Modular robotic platform for precision neurosurgery with a bio-inspired needle: System overview and first in-vivo deployment.

Over the past 10 years, minimally invasive surgery (MIS) has shown significant benefits compared to conventional surgical techniques, with reduced tra...

The application of artificial intelligence to support biliary atresia screening by ultrasound images: A study based on deep learning models.

PURPOSE: Early confirmation or ruling out biliary atresia (BA) is essential for infants with delayed onset of jaundice. In the current practice, percu...

Validation of Combined Deep Learning Triaging and Computer-Aided Diagnosis in 2901 Breast MRI Examinations From the Second Screening Round of the Dense Tissue and Early Breast Neoplasm Screening Trial.

OBJECTIVES: Computer-aided triaging (CAT) and computer-aided diagnosis (CAD) of screening breast magnetic resonance imaging have shown potential to re...

Biological signatures and prediction of an immunosuppressive status-persistent critical illness-among orthopedic trauma patients using machine learning techniques.

BACKGROUND: Persistent critical illness (PerCI) is an immunosuppressive status. The underlying pathophysiology driving PerCI remains incompletely unde...

Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review.

There has been an explosive growth in research over the last decade exploring machine learning techniques for analyzing chest X-ray (CXR) images for s...