Emergency Medicine

Prediagnosis recognition of acute ischemic stroke by artificial intelligence from facial images.

Stroke is a major threat to life and health in modern society, especially in the aging population. Stroke may cause sudden death or severe sequela-like hemiplegia. Although computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis methods, and artificial intelligence models have been built based on these images, shortage in medical resources and the time and cost of CT/M...

Interpretable machine learning predicts postpartum hemorrhage with severe maternal morbidity in a lower-risk laboring obstetric population.

BACKGROUND: Early identification of patients at increased risk for postpartum hemorrhage (PPH) associated with severe maternal morbidity (SMM) is critical for preparation and preventative intervention. However, prediction is challenging in patients without obvious risk factors for postpartum hemorrhage with severe maternal morbidity. Current tools for hemorrhage risk assessment use lists of risk f...

A Novel Machine Learning Model for Predicting Stroke-Associated Pneumonia After Spontaneous Intracerebral Hemorrhage.

BACKGROUND: Pneumonia is one of the most common complications after spontaneous intracerebral hemorrhage (sICH), i.e., stroke-associated pneumonia (SA...

Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms.

In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on ...

Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms.

Early prognostication of patient outcomes in intracerebral hemorrhage (ICH) is critical for patient care. We aim to investigate protein biomarkers' ro...

Machine learning-based model for predicting outcomes in cerebral hemorrhage patients with leukemia.

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) in leukemia patients progresses rapidly with high mortality. Limited data are available on imagi...

Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis.

BACKGROUND: Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke in noncontrast computed tomography (NCCT) scans a...

Machine learning based peri-surgical risk calculator for abdominal related emergency general surgery: a multicenter retrospective study.

BACKGROUND: Currently, there is a lack of ideal risk prediction tools in the field of emergency general surgery (EGS). The American Association for th...

ChatGPT in medicine: prospects and challenges: a review article.

It has been a year since the launch of Chat Generator Pre-Trained Transformer (ChatGPT), a generative artificial intelligence (AI) program. The introd...

A novel case-finding strategy based on artificial intelligence for the systematic identification and management of individuals with osteoporosis or at varying risk of fragility fracture.

UNLABELLED: An artificial intelligence-based case-finding strategy has been developed to systematically identify individuals with osteoporosis or at v...

Appropriate use of blood cultures in the emergency department through machine learning (ABC): study protocol for a randomised controlled non-inferiority trial.

INTRODUCTION: The liberal use of blood cultures in emergency departments (EDs) leads to low yields and high numbers of false-positive results. False-p...

Leveraging graph neural networks for supporting automatic triage of patients.

Patient triage is crucial in emergency departments, ensuring timely and appropriate care based on correctly evaluating the emergency grade of patient ...

Can machine learning predict late seizures after intracerebral hemorrhages? Evidence from real-world data.

INTRODUCTION: Intracerebral hemorrhage represents 15 % of all strokes and it is associated with a high risk of post-stroke epilepsy. However, there ar...

Unlocking the adaptive advantage: correlation and machine learning classification to identify optimal online adaptive stereotactic partial breast candidates.

Online adaptive radiotherapy (OART) is a promising technique for delivering stereotactic accelerated partial breast irradiation (APBI), as lumpectomy ...

Machine learning model based on radiomics features for AO/OTA classification of pelvic fractures on pelvic radiographs.

Depending on the degree of fracture, pelvic fracture can be accompanied by vascular damage, and in severe cases, it may progress to hemorrhagic shock....

Assessing the utility of artificial intelligence throughout the triage outpatients: a prospective randomized controlled clinical study.

Currently, there are still many patients who require outpatient triage assistance. ChatGPT, a natural language processing tool powered by artificial i...

Identification of biological indicators for human exposure toxicology in smart cities based on public health data and deep learning.

With the acceleration of urbanization, the risk of urban population exposure to environmental pollutants is increasing. Protecting public health is th...

Fillable Magnetic Microrobots for Drug Delivery to Cardiac Tissues In Vitro.

Many cardiac diseases, such as arrhythmia or cardiogenic shock, cause irregular beating patterns that must be regulated to prevent disease progression...

Knowledge, attitudes and practices of using Indocyanine Green (ICG) fluorescence in emergency surgery: an international web-based survey in the ARtificial Intelligence in Emergency and trauma Surgery (ARIES)-WSES project.

Fluorescence imaging is a real-time intraoperative navigation modality to enhance surgical vision and it can guide emergency surgeons while performing...

Proximal femur fracture detection on plain radiography via feature pyramid networks.

Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240-310% by 2050. Hip fracture...