Emergency Medicine

Establishment and validation of an artificial intelligence web application for predicting postoperative in-hospital mortality in patients with hip fracture: a national cohort study of 52 707 cases.

BACKGROUND: In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practice. By utilizing artificial intelligence (AI) and machine learning techniques, this study aims to develop a predictive model that can assist clinicians...

Revolutionizing Aneurysm detection: The role of artificial intelligence in reducing rupture rates.

Cerebral aneurysms, affecting 2-5% of the global population, are often asymptomatic and commonly located within the Circle of Willis. A recent study in Neurosurgical Review highlights a significant reduction in the annual rupture rates of unruptured cerebral aneurysms (UCAs) in Japan from 2003 to 2018. By analyzing age-adjusted mortality rates of subarachnoid hemorrhage (SAH) and the number of tre...

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...

Single-center outcomes of artificial intelligence in management of pulmonary embolism and pulmonary embolism response team activation.

Multidisciplinary pulmonary embolism response teams (PERTs) have shown that timely triage expedites treatment. The use of artificial intelligence (AI)...

Advancements in prognostic markers and predictive models for intracerebral hemorrhage: from serum biomarkers to artificial intelligence models.

Intracerebral hemorrhage (ICH) is a severe form of stroke with high morbidity and mortality, accounting for 10-15% of all strokes globally. Recent adv...

Predictive modeling of mortality in carbapenem-resistant bloodstream infections using machine learning.

, a notable drug-resistant bacterium, often induces severe infections in healthcare settings, prompting a deeper exploration of treatment alternatives...

Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department.

BACKGROUND AND OBJECTIVES: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue med...

AFFnet - a deep convolutional neural network for the detection of atypical femur fractures from anteriorposterior radiographs.

Despite well-defined criteria for radiographic diagnosis of atypical femur fractures (AFFs), missed and delayed diagnosis is common. An AFF diagnostic...

Development and validation of a machine learning framework for improved resource allocation in the emergency department.

OBJECTIVE: The Emergency Severity Index (ESI) is the most commonly used system in over 70% of all U.S. emergency departments (ED) that uses predicted ...

A proposal for cut marks classification using machine learning: Serrated vs. non-serrated, single vs. double-beveled knives.

In tool mark identification, there is still a lack of characteristics and methodologies standardization used to analyze and describe sharp force traum...

Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion.

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide vari...

Understanding natural language: Potential application of large language models to ophthalmology.

Large language models (LLMs), a natural language processing technology based on deep learning, are currently in the spotlight. These models closely mi...

Healthcare leaders' experiences of implementing artificial intelligence for medical history-taking and triage in Swedish primary care: an interview study.

BACKGROUND: Artificial intelligence (AI) holds significant promise for enhancing the efficiency and safety of medical history-taking and triage within...

A Deep-Learning-Based CPR Action Standardization Method.

In emergency situations, ensuring standardized cardiopulmonary resuscitation (CPR) actions is crucial. However, current automated external defibrillat...

Using ensemble learning and hierarchical strategy to predict the outcomes of ESWL for upper ureteral stone treatment.

Urinary tract stones are a common and frequently recurring medical issue. Accurately predicting the success rate after surgery can help avoid ineffect...

Artificial intelligence/machine learning for neuroimaging to predict hemorrhagic transformation: Systematic review/meta-analysis.

BACKGROUND AND PURPOSE: Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for t...

Deploying artificial intelligence in the detection of adult appendicular and pelvic fractures in the Singapore emergency department after hours: efficacy, cost savings and non-monetary benefits.

INTRODUCTION: Radiology plays an integral role in fracture detection in the emergency department (ED). After hours, when there are fewer reporting rad...

An overview of diagnostics and therapeutics using large language models.

There is an acute need for solutions to treat stress and trauma-related sequelae, and there are well-documented shortages of qualified human professio...

Diagnostic Accuracy of Ultra-Low Dose CT Compared to Standard Dose CT for Identification of Fresh Rib Fractures by Deep Learning Algorithm.

The present study aimed to evaluate the diagnostic accuracy of ultra-low dose computed tomography (ULD-CT) compared to standard dose computed tomograp...

[Supporting medical and nursing activities with AI: recommendations for responsible design and use].

Clinical decision support systems (CDSS) based on artificial intelligence (AI) are complex socio-technical innovations and are increasingly being used...