Emergency Medicine

A machine learning-based Coagulation Risk Index predicts acute traumatic coagulopathy in bleeding trauma patients.

BACKGROUND: Acute traumatic coagulopathy (ATC) is a well-described phenomenon known to begin shortly after injury. This has profound implications for resuscitation from hemorrhagic shock, as ATC is associated with increased risk for massive transfusion (MT) and mortality. We describe a large-data machine learning-based Coagulation Risk Index (CRI) to test the early prediction of ATC in bleeding tr...

Empowering surgeons: will artificial intelligence change oral and maxillofacial surgery?

Artificial Intelligence (AI) can enhance the precision and efficiency of diagnostics and treatments in oral and maxillofacial surgery (OMS), leveraging advanced computational technologies to mimic intelligent human behaviors. The study aimed to examine the current state of AI in the OMS literature and highlight the urgent need for further research to optimize AI integration in clinical practice an...

Impact of a deep learning-based brain CT interpretation algorithm on clinical decision-making for intracranial hemorrhage in the emergency department.

Intracranial hemorrhage is a critical emergency that requires prompt and accurate diagnosis in the emergency department (ED). Deep learning technology...

Factors predicting access to medications for opioid use disorder for housed and unhoused patients: A machine learning approach.

BACKGROUND: Opioid use disorder (OUD) is a growing public health crisis, with opioids involved in an overwhelming majority of drug overdose deaths in ...

An Artificial Intelligence Algorithm Integrated into the Clinical Workflow Can Ensure High Quality Acute Intracranial Hemorrhage CT Diagnostic.

PURPOSE: Intracranial hemorrhage (ICH) is a life-threatening condition requiring rapid diagnostic and therapeutic action. This study evaluates whether...

Using Natural Language Processing (GPT-4) for Computed Tomography Image Analysis of Cerebral Hemorrhages in Radiology: Retrospective Analysis.

BACKGROUND: Cerebral hemorrhage is a critical medical condition that necessitates a rapid and precise diagnosis for timely medical intervention, inclu...

Evaluating the accuracy and reliability of AI chatbots in disseminating the content of current resuscitation guidelines: a comparative analysis between the ERC 2021 guidelines and both ChatGPTs 3.5 and 4.

AIM OF THE STUDY: Artificial intelligence (AI) chatbots are established as tools for answering medical questions worldwide. Healthcare trainees are in...

[How well does artificial intelligence detect fractures in the cervical spine on CT?].

OBJECTIVE: To compare diagnostic accuracy of artificial intelligence (AI) for cervical spine (C-spine) fracture detection on CT with attending radiolo...

Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage.

Intraventricular hemorrhage (IVH) in preterm neonates presents a high risk for developing posthemorrhagic ventricular dilatation (PHVD), a severe comp...

Artificial Intelligence to Predict Billing Code Levels of Emergency Department Encounters.

STUDY OBJECTIVE: To use artificial intelligence (AI) to predict billing code levels for emergency department (ED) encounters.

Development and validation of a machine-learning model for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage.

Pneumonia is a common postoperative complication in patients with aneurysmal subarachnoid hemorrhage (aSAH), which is associated with poor prognosis a...

A comprehensive comparison of machine learning models for ICH prognostication: Retrospective review of 1501 intra-cerebral hemorrhage patients from the Qatar stroke database.

Multiple prognostic scores have been developed to predict morbidity and mortality in patients with spontaneous intracerebral hemorrhage(sICH). Since t...

Evolution of radiology staff perspectives during artificial intelligence (AI) implementation for expedited lung cancer triage.

AIMS: To investigate radiology staff perceptions of an AI tool for chest radiography triage, flagging findings suspicious for lung cancer to expedite ...

Enhanced Osteoporosis Detection Using Artificial Intelligence: A Deep Learning Approach to Panoramic Radiographs with an Emphasis on the Mental Foramen.

Osteoporosis, a skeletal disorder, is expected to affect 60% of women aged over 50 years. Dual-energy X-ray absorptiometry (DXA) scans, the current go...

Factors associated with 90-day mortality in Vietnamese stroke patients: Prospective findings compared with explainable machine learning, multicenter study.

The prevalence and predictors of mortality following an ischemic stroke or intracerebral hemorrhage have not been well established among patients in V...

Interpretable prediction of acute ischemic stroke after hip fracture in patients 65 years and older based on machine learning and SHAP.

BACKGROUND: Hip fracture and acute ischemic stroke (AIS) are prevalent conditions among the older population. The prognosis for older patients who exp...

Coagulo-Net: Enhancing the mathematical modeling of blood coagulation using physics-informed neural networks.

Blood coagulation, which involves a group of complex biochemical reactions, is a crucial step in hemostasis to stop bleeding at the injury site of a b...

Artificial intelligence in paediatric head trauma: enhancing diagnostic accuracy for skull fractures and brain haemorrhages.

Pediatric head trauma is a significant cause of morbidity and mortality, with children, particularly those under two years old, being more susceptible...

The potential benefit of artificial intelligence regarding clinical decision-making in the treatment of wrist trauma patients.

PURPOSE: The implementation of artificial intelligence (AI) in health care is gaining popularity. Many publications describe powerful AI-enabled algor...

Machine learning-based prediction of the risk of moderate-to-severe catheter-related bladder discomfort in general anaesthesia patients: a prospective cohort study.

BACKGROUND: Catheter-related bladder discomfort (CRBD) commonly occurs in patients who have indwelling urinary catheters while under general anesthesi...