Emergency Medicine

Artificial intelligence for predicting shockable rhythm during cardiopulmonary resuscitation: In-hospital setting.

AIM OF THE STUDY: This study aimed to develop an artificial intelligence (AI) model capable of predicting shockable rhythms from electrocardiograms (ECGs) with compression artifacts using real-world data from emergency department (ED) settings. Additionally, we aimed to explore the black box nature of AI models, providing explainability.

Machine learning models predict triage levels, massive transfusion protocol activation, and mortality in trauma utilizing patients hemodynamics on admission.

BACKGROUND: The effective management of trauma patients necessitates efficient triaging, timely activation of Massive Blood Transfusion Protocols (MTP), and accurate prediction of in-hospital outcomes. Machine learning (ML) algorithms have emerged as up-and-coming tools in the domains of optimizing triage decisions, improving intervention strategies, and predicting clinical outcomes, consistently ...

Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery.

Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at l...

Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using a multimodal neural network.

Hematoma expansion occasionally occurs in patients with intracerebral hemorrhage (ICH), associating with poor outcome. Multimodal neural networks inco...

Versatile Deep Learning Pipeline for Transferable Chemical Data Extraction.

Chemical information disseminated in scientific documents offers an untapped potential for deep learning-assisted insights and breakthroughs. Automate...

Advancing grading and outcome prediction in aneurysmal subarachnoid hemorrhage: Harnessing artificial intelligence and machine learning for precision healthcare.

Deep learning application of vertebral compression fracture detection using mask R-CNN.

Vertebral compression fractures (VCFs) of the thoracolumbar spine are commonly caused by osteoporosis or result from traumatic events. Early diagnosis...

Automated Method for Intracranial Aneurysm Classification Using Deep Learning.

Intracranial aneurysm (IA) is now a common term closely associated with subarachnoid hemorrhage. IA is the bulging of a blood vessel caused by a weake...

A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.

BACKGROUND: With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could ...

Analyzing pain patterns in the emergency department: Leveraging clinical text deep learning models for real-world insights.

OBJECTIVE: To determine the incidence of patients presenting in pain to a large Australian inner-city emergency department (ED) using a clinical text ...

Predicting Secondary Vertebral Compression Fracture After Vertebral Augmentation via CT-Based Machine Learning Radiomics-Clinical Model.

RATIONALE AND OBJECTIVES: Secondary vertebral compression fractures (SVCF) are very common in patients after vertebral augmentation (VA). The aim of t...

Deep Learning-Based Localization and Detection of Malpositioned Nasogastric Tubes on Portable Supine Chest X-Rays in Intensive Care and Emergency Medicine: A Multi-center Retrospective Study.

Malposition of a nasogastric tube (NGT) can lead to severe complications. We aimed to develop a computer-aided detection (CAD) system to localize NGTs...

Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal.

BACKGROUND: It was essential to identify individuals at high risk of fragility fracture and prevented them due to the significant morbidity, mortality...

Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline.

Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindi...

An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.

OBJECTIVES: To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features...

Artificial intelligence-based evaluation of carotid artery compressibility via point-of-care ultrasound in determining the return of spontaneous circulation during cardiopulmonary resuscitation.

AIM: This study introduces RealCAC-Net, an artificial intelligence (AI) system, to quantify carotid artery compressibility (CAC) and determine the ret...

Multi-scale object equalization learning network for intracerebral hemorrhage region segmentation.

Segmentation and the subsequent quantitative assessment of the target object in computed tomography (CT) images provide valuable information for the a...

Evaluation of Online AI-Generated Foot and Ankle Surgery Information.

As a natural progression from educational pamphlets to the worldwide web, and now artificial intelligence (AI), OpenAI chatbots provide a simple way o...

DeepSAP: A Novel Brain Image-Based Deep Learning Model for Predicting Stroke-Associated Pneumonia From Spontaneous Intracerebral Hemorrhage.

RATIONALE AND OBJECTIVE: Stroke-associated pneumonia (SAP) often appears as a complication following intracerebral hemorrhage (ICH), leading to poor p...

Machine learning predicts emergency physician specialties from treatment strategies for patients suspected of myocardial infarction.

BACKGROUND: Our investigation aimed to determine how the diverse backgrounds and medical specialties of emergency physicians (Eps) influence the accur...