Emergency Medicine

A novel technology integrating robotics and 3D printing for closed reduction of tibia shaft fracture with MIPPO:A proof-of-concept study.

Less invasive fixation techniques, such as intramedullary nailing (IMN) and minimally invasive percutaneous plate osteosynthesis (MIPPO), are now the preferred choices for treating tibia shaft fractures (TSFs). However, malreduction and radiation exposure are the main deficiencies associated with less invasive fixation techniques, especially when assessing rotation around the shaft axis intra-oper...

Review of machine learning and deep learning models for toxicity prediction.

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional and toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods...

Retracted: X-Ray Film under Artificial Intelligence Algorithm in the Evaluation for Nursing Effect of Gamma Nail Internal Fixation in Elderly Patients with Intertrochanteric Fracture of Femur.

[This retracts the article DOI: 10.1155/2021/2562575.].

The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis.

Bone ranks as the third most frequent tissue affected by cancer metastases, following the lung and liver. Bone metastases are often painful and may re...

Attitudes towards artificial intelligence in emergency medicine.

OBJECTIVE: To assess Australian and New Zealand emergency clinicians' attitudes towards the use of artificial intelligence (AI) in emergency medicine.

Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances.

The market for illicit drugs has been reshaped by the emergence of more than 1100 new psychoactive substances (NPS) over the past decade, posing a maj...

Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study.

BACKGROUND: This systematic review and meta-analysis were conducted to objectively evaluate the evidence of machine learning (ML) in the patient diagn...

Unveiling the potential of artificial intelligence in orthopaedic surgery.

Artificial intelligence is paving the way in contemporary medical advances, with the potential to revolutionise orthopaedic surgical care. By harnessi...

Clinical Characteristics and Outcomes of Patients with Intracerebral Hemorrhage: Nonsurgical Versus Surgical Treatment.

INTRODUCTION: Primary intracerebral hemorrhage (ICH) is a stroke subtype with high morbidity and mortality. Surgical treatments for ICH may be one of ...

The Influence of the Novel Computer-Aided Triage System Based on Artificial Intelligence on Endovascular Therapy in Patients with Large Vascular Occlusions: A Meta-Analysis.

OBJECTIVE: The integration of artificial intelligence (AI) with modern healthcare has become increasingly prominent. The purpose of this study is to e...

Predicting incomplete occlusion of intracranial aneurysms treated with flow diverters using machine learning models.

OBJECTIVE: Intracranial saccular aneurysms are vascular malformations responsible for 80% of nontraumatic brain hemorrhage. Recently, flow diverters h...

Minimally invasive percutaneous screw internal fixation under robot navigation for the treatment of a hamate bone fracture.

PURPOSE: Hamate fractures are rare fractures of the wrist and there is still no consensus on the optimal treatment for these fractures, especially hoo...

The use of POTTER (Predictive Optimal Trees in Emergency Surgery Risk) calculator to predict mortality and complications in patients submitted to Emergency Surgery.

INTRODUCTION: the ability of the care team to reliably predict postoperative risk is essential for improvements in surgical decision-making, patient a...

Machine learning models for early prediction of mortality risk in patients with burns: A single center experience.

Mortality rate is considered as the most important outcome measure for assessing the severity of burn injury. A scale or model that accurately predict...

Antiproliferative Cancer Cell and Fungicidal Effects of Yellow and Red Araçá ( Sabine) Fruit Extract.

Araçá is a native Brazil fruit, and has two morphological types, yellow and red; however, it is still little consumed by the population. Although ther...

The application of artificial intelligence in the management of sepsis.

Sepsis is a complex and heterogeneous syndrome that remains a serious challenge to healthcare worldwide. Patients afflicted by severe sepsis or septic...

Informed consent for artificial intelligence in emergency medicine: A practical guide.

As artificial intelligence (AI) expands its presence in healthcare, particularly within emergency medicine (EM), there is growing urgency to explore t...

Prognostication of Outcomes in Spontaneous Intracerebral Hemorrhage: A Propensity Score-Matched Analysis with Support Vector Machine.

OBJECTIVE: The role of surgery in spontaneous intracerebral hemorrhage (SICH) remains controversial. We aimed to use explainable machine learning (ML)...

Deep-Learning Automation of Preoperative Radiographic Parameters Associated With Early Periprosthetic Femur Fracture After Total Hip Arthroplasty.

BACKGROUND: The radiographic assessment of bone morphology impacts implant selection and fixation type in total hip arthroplasty (THA) and is importan...

Assessing the Potential of a Deep Learning Tool to Improve Fracture Detection by Radiologists and Emergency Physicians on Extremity Radiographs.

RATIONALE AND OBJECTIVES: To evaluate the standalone performance of a deep learning (DL) based fracture detection tool on extremity radiographs and as...