Emergency Medicine

Latest AI and machine learning research in emergency medicine for healthcare professionals.

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Computer tomography-based radiomics combined with machine learning for predicting the time since onset of epidural hematoma.

Estimation of the age of epidural hematoma (EDH) is a challenge in clinical forensic medicine, and t...

Application and Prospects of Deep Learning Technology in Fracture Diagnosis.

Artificial intelligence (AI) is an interdisciplinary field that combines computer technology, mathem...

Application of artificial intelligence in triage in emergencies and disasters: a systematic review.

INTRODUCTION AND OBJECTIVE: Modern and intelligent triage systems are used today due to the growing ...

Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review.

BACKGROUND: In Emergency Departments (EDs), triage is crucial for determining patient severity and p...

Comparative efficacy of anteroposterior and lateral X-ray based deep learning in the detection of osteoporotic vertebral compression fracture.

Magnetic resonance imaging remains the gold standard for diagnosing osteoporotic vertebral compressi...

ResNet-Transformer deep learning model-aided detection of dens evaginatus.

BACKGROUND: Dens evaginatus is a dental morphological developmental anomaly. Failing to detect it ma...

Enhancing systematic review efficiency in hand surgery using artificial intelligence (natural language processing) for abstract screening.

The aim of the present study was to train a natural language processing model to recognize key text ...

Artificial intelligence in fracture detection on radiographs: a literature review.

Fractures are one of the most common reasons of admission to emergency department affecting individu...

Exploring ChatGPT's potential in ECG interpretation and outcome prediction in emergency department.

BACKGROUND: Approximately 20 % of emergency department (ED) visits involve cardiovascular symptoms. ...

Automatic AI tool for opportunistic screening of vertebral compression fractures on chest frontal radiographs: A multicenter study.

Vertebral compression fractures (VCFs) are the most common type of osteoporotic fractures, yet they ...

Cultivating diagnostic clarity: The importance of reporting artificial intelligence confidence levels in radiologic diagnoses.

Accurate image interpretation is essential in the field of radiology to the healthcare team in order...

Advantages of Metabolomics-Based Multivariate Machine Learning to Predict Disease Severity: Example of COVID.

The COVID-19 outbreak caused saturations of hospitals, highlighting the importance of early patient ...

CT-based deep learning model for predicting the success of extracorporeal shock wave lithotripsy in treating ureteral stones larger than 1 cm.

OBJECTIVES: To develop a deep learning (DL) model based on computed tomography (CT) images to predic...

Detection, classification, and characterization of proximal humerus fractures on plain radiographs.

AIMS: The purpose of this study was to develop a convolutional neural network (CNN) for fracture det...

Optimization of grinding parameters in robotic-assisted preparation of cracked teeth based on fracture mechanics: FEA and experiment.

BACKGROUND AND OBJECTIVES: If left untreated, cracked teeth can lead to tooth loss, of which the inc...

Deep Learning Approaches for the Assessment of Germinal Matrix Hemorrhage Using Neonatal Head Ultrasound.

Germinal matrix hemorrhage (GMH) is a critical condition affecting premature infants, commonly diagn...

Predicting burn probability: Dimensionality reduction strategies enable accurate and computationally efficient metamodeling.

Predicting the probability that a given location will be burnt by a wildfire is an important part of...

Deep learning-assisted detection of psychoactive water pollutants using behavioral profiling of zebrafish embryos.

Water pollution poses a significant risk to the environment and human health, necessitating the deve...

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