Emergency Medicine

Artificial intelligence-informed planning for the rapid response of hazard-impacted road networks.

Post-hazard rapid response has emerged as a promising pathway towards resilient critical infrastructure systems (CISs). Nevertheless, it is challenging to scheme the optimal plan for those rapid responses, given the enormous search space and the hardship of assessment on the spatiotemporal status of CISs. We now present a new approach to post-shock rapid responses of road networks (RNs), based upo...

Application of supervised machine learning algorithms to predict the risk of hidden blood loss during the perioperative period in thoracolumbar burst fracture patients complicated with neurological compromise.

BACKGROUND: Machine learning (ML) is a type of artificial intelligence (AI) and has been utilized in clinical research and practice to construct high-performing prediction models. Hidden blood loss (HBL) is prevalent during the perioperative period of spinal treatment and might result in a poor prognosis. The aim of this study was to develop a ML-based model for identifying perioperative HBL-relat...

Detection of Tip-Burn Stress on Lettuce Grown in an Indoor Environment Using Deep Learning Algorithms.

Lettuce grown in indoor farms under fully artificial light is susceptible to a physiological disorder known as tip-burn. A vital factor that controls ...

How do people think about the implementation of speech and video recognition technology in emergency medical practice?

BACKGROUND: Recently, speech and video information recognition technology (SVRT) has developed rapidly. Introducing SVRT into the emergency medical pr...

Imaging of the optic nerve: technological advances and future prospects.

Over the past decade, ocular imaging strategies have greatly advanced the diagnosis and follow-up of patients with optic neuropathies. Developments in...

Efficacy and pitfalls of digital technologies in healthcare services: A systematic review of two decades.

The use of technology in the healthcare sector and its medical practices, from patient record maintenance to diagnostics, has significantly improved t...

Prescreening and Triage of COVID-19 Patients Through Chest X-Ray Images Using Deep Learning Model.

Deep learning models deliver a fast diagnosis during triage prescreening for COVID-19 patients, reducing waiting time for hospital admission during he...

Deep Learning Assistance Closes the Accuracy Gap in Fracture Detection Across Clinician Types.

BACKGROUND: Missed fractures are the most common diagnostic errors in musculoskeletal imaging and can result in treatment delays and preventable morbi...

Real-world Implementation of an eHealth System Based on Artificial Intelligence Designed to Predict and Reduce Emergency Department Visits by Older Adults: Pragmatic Trial.

BACKGROUND: Frail older people use emergency services extensively, and digital systems that monitor health remotely could be useful in reducing these ...

Predictive nomogram for soft robotic hand rehabilitation of patients with intracerebral hemorrhage.

BACKGROUND: Few studies focused on the risk factors for hand rehabilitation of intracerebral hemorrhage (ICH) using of soft robotic hand therapy (SRHT...

Automatic Detection of Cases of COVID-19 Pneumonia from Chest X-ray Images and Deep Learning Approaches.

Machine learning has already been used as a resource for disease detection and health care as a complementary tool to help with various daily health c...

Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images.

OBJECTIVES: Evaluating the diagnostic efficiency of deep learning models to diagnose vertical root fracture in vivo on cone-beam CT (CBCT) images.

Training in the protocol for robotic undocking for life emergency support (RULES) improves team communication, coordination and reduces the time required to decouple the robotic system from the patient.

INTRODUCTION: Robotic surgery has expanded on it's surgical application and it is also noted an increase in surgical procedures complexity. Occurrence...

BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources.

INTRODUCTION: Background field removal (BFR) is a critical step required for successful quantitative susceptibility mapping (QSM). However, eliminatin...

Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM).

Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determin...

Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods.

BACKGROUND: COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical servic...

Improvement of Patient Classification Using Feature Selection Applied to Bidirectional Axial Transmission.

Osteoporosis is still a worldwide problem, particularly due to associated fragility fractures. Patients at risk of fracture are currently detected usi...

Exposure to nitrate induces alterations in blood parameter responses, liver immunity, and lipid metabolism in juvenile turbot (Scophthalmus maximus).

Nitrate (NO) pollution of waterbodies has attracted significant global attention as it poses a serious threat to aquatic organisms and human beings. T...

Identification of microstructures critically affecting material properties using machine learning framework based on metallurgists' thinking process.

In materials science, machine learning has been intensively researched and used in various applications. However, it is still far from achieving intel...

A Continuum Robotic Cannula With Tip Following Capability and Distal Dexterity for Intracerebral Hemorrhage Evacuation.

OBJECTIVE: This paper aims to investigate a new continuum robot design and its motion implementation methods appropriate for a minimally invasive intr...