Emergency Medicine

Smartphone pupillometry with machine learning differentiates ischemic from hemorrhagic stroke: A pilot study.

OBJECTIVES: Similarities between acute ischemic and hemorrhagic stroke make diagnosis and triage challenging. We studied a smartphone-based quantitative pupillometer for differentiation of acute ischemic and hemorrhagic stroke.

Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models.

BACKGROUND: Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, with hemorrhagic transformation (HT) further worsening outcomes. Traditional scoring systems have limited predictive accuracy for HT in AIS. Recent research has explored machine learning (ML) and deep learning (DL) algorithms for stroke management. This study evaluates and compares the effectiveness of ML and DL a...

Development and Clinical Validation of Visual Inspection With Acetic Acid Application-Artificial Intelligence Tool Using Cervical Images in Screen-and-Treat Visual Screening for Cervical Cancer in South India: A Pilot Study.

PURPOSE: The burden of cervical cancer in India is enormous, with more than 60,000 deaths being reported in 2020. The key intervention in the WHO's gl...

Cross-disease transcriptomic analysis reveals DOK3 and PAPOLA as therapeutic targets for neuroinflammatory and tumorigenic processes.

OBJECTIVE: Subarachnoid hemorrhage (SAH) and tumorigenesis share numerous biological complexities; nevertheless, the specific gene expression profiles...

Factors influencing vancomycin trough concentration in burn patients: a single center retrospective study.

OBJECTIVE: To analyze factors influencing the vancomycin trough concentration in burn patients to provide a basis for the more rational use of vancomy...

Cyto-Safe: A Machine Learning Tool for Early Identification of Cytotoxic Compounds in Drug Discovery.

Cytotoxicity is essential in drug discovery, enabling early evaluation of toxic compounds during screenings to minimize toxicological risks. assays s...

Integrating GPT-Based AI into Virtual Patients to Facilitate Communication Training Among Medical First Responders: Usability Study of Mixed Reality Simulation.

BACKGROUND: Training in social-verbal interactions is crucial for medical first responders (MFRs) to assess a patient's condition and perform urgent t...

AI frontiers in emergency care: the next evolution of nursing interventions.

This scoping review explores the utilization of artificial intelligence in emergency nursing, assessing its impact, potential benefits, and the obstac...

Artificial intelligence-assisted echocardiographic monitoring in pediatric patients on extracorporeal membrane oxygenation.

BACKGROUND: Percutaneous extracorporeal membrane oxygenation (ECMO) is administered to pediatric patients with cardiogenic shock or cardiac arrest. Th...

Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.

BACKGROUND: Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effec...

Effective analysis of thyroid toxicity and mechanisms of acetyltributyl citrate using network toxicology, molecular docking, and machine learning strategies.

The growing prevalence of environmental pollutants has raised concerns about their potential role in thyroid dysfunction and related disorders. Previo...

An AI-Driven Framework for Detecting Bone Fractures in Orthopedic Therapy.

This study presents an advanced artificial intelligence-driven framework designed to enhance the speed and accuracy of bone fracture detection, addres...

Fatal fall from a height: is it possible to apply artificial intelligence techniques for height estimation?

Fall from a height trauma is characterized by a multiplicity of injuries, related to multiple factors. The height of the fall is the factor that most ...

Artificial intelligence (AI) for paediatric fracture detection: a multireader multicase (MRMC) study protocol.

INTRODUCTION: Paediatric fractures are common but can be easily missed on radiography leading to potentially serious implications including long-term ...

Prediction of Symptomatic Intracranial Hemorrhage Before Mechanical Thrombectomy Using Machine Learning in Patients with Anterior Circulation Large Vessel Occlusion.

BACKGROUND: Symptomatic intracranial hemorrhage (sICH) after mechanical thrombectomy (MT) is associated with worse outcomes. We sought to develop and ...

A machine learning-based risk score for prediction of mechanical ventilation in children with dengue shock syndrome: A retrospective cohort study.

BACKGROUND: Patients with severe dengue who develop severe respiratory failure requiring mechanical ventilation (MV) support have significantly increa...

Early prediction of intensive care unit admission in emergency department patients using machine learning.

BACKGROUND: The timely identification and transfer of critically ill patients from the emergency department (ED) to the intensive care unit (ICU) is i...

Machine learning for predicting poor outcomes in aneurysmal subarachnoid hemorrhage: A systematic review and meta-analysis involving 8445 participants.

Early prediction of poor outcomes in patients impacted with aneurysmal subarachnoid hemorrhage (aSAH) is crucial for timely intervention and effective...

Integrating structured and unstructured data for predicting emergency severity: an association and predictive study using transformer-based natural language processing models.

BACKGROUND: Efficient triage in emergency departments (EDs) is critical for timely and appropriate care. Traditional triage systems primarily rely on ...

Accuracy of radiologists and radiology residents in detection of paediatric appendicular fractures with and without artificial intelligence.

OBJECTIVES: We aim to evaluate the accuracy of radiologists and radiology residents in the detection of paediatric appendicular fractures with and wit...