Emergency Medicine

Machine Learning for Predicting Stroke Risk Stratification Using Multiomics Data: Systematic Review.

BACKGROUND: Stroke is a complex, multidimensional disorder influenced by interacting inflammatory, immune, coagulation, endothelial, and metabolic pathways. Single-omics approaches seldom capture this complexity, whereas multiomics techniques provide complementary insights but generate high-dimensional and correlated feature spaces. Machine learning (ML) offers strategies to manage these challenge...

Automated Risk Assessment of Opioid Use: Analysis Using Pre-Trained Transformers on Social Media Data.

BACKGROUND: The illegal use of opioids has emerged as a major global public health concern, contributing to widespread addiction and a growing number of overdose-related deaths. In response, the US federal government has invested billions of dollars in combating the opioid epidemic through treatment, prevention, and law enforcement initiatives. Despite these efforts, there remains an urgent need f...

HEDGE Index: A Data-Driven Framework Overcoming the Limitations of TEFs for Prioritizing Polycyclic Aromatic Hydrocarbon Risks.

Conventional risk assessment of airborne polycyclic aromatic hydrocarbons (PAHs), based on carcinogenicity-focused toxic equivalency factors (TEFs), l...

Ultra-Fast Sub-Minute Brain MRI with Deep-Learning-reconstruction for Anesthesia-Free Emergency Imaging in Children.

PURPOSE: As MRI frequently requires general anesthesia in pediatric patients, there is an undersupply in clinical routine. This may result in delayed ...

Development of a 3D Convolutional Neural Network for the Triage of High-Priority Oral and Maxillofacial CBCT Scans.

This project aims to establish a triage system for oral and maxillofacial cone-beam computed tomography (CBCT) scans by developing a neural network to...

Advancements in ex vivo bone biomechanics: multimodal technologies and their integration with artificial intelligence.

Bone biomechanics is essential for understanding how bone structure, composition, and mechanical function interact in healthy and diseased states. Con...

Development and validation of an in-hospital cardiogenic shock prediction model for AMI patients based on machine learning.

Prediction of hemorrhagic transformation after intravenous thrombolysis in acute anterior circulation ischemic stroke through radiomics-based machine learning models.

BACKGROUND: Hemorrhagic transformation (HT) and its associated neurological deterioration remain major concerns that limit decision-making in thrombol...

An Artificial Intelligence-Selected Multifunctional Oligopeptide Coated Biphasic Calcium Phosphate (BCP) Scaffold Promotes Bone Defect Repair.

Bone defects caused by inflammation, trauma, tumor resection, or tooth extraction present persistent clinical challenges, primarily because of the lim...

Landmark-based deep learning for radiographic screening for developmental dysplasia of the hip in infants: Development and external evaluation with IHDI-guided triage.

BACKGROUND: In Japan, secondary screening for developmental hip dysplasia has expanded. However, the capacity of screening programs has outpaced the a...

Optimization and External Validation of a Deep Learning Model for Segmentation and Quantification of Traumatic Brain Injury Lesions on Head Computed Tomography.

Current methods for radiological classification in traumatic brain injury (TBI), such as the Marshall and Rotterdam score, provide an incomplete descr...

Where New and Old Technologies Converge: In Search of a Better Way to Predict Pathologic Fracture: Commentary on an article by Shinn Kim, MD, et al.: "Deep Learning Model for Differentiating Between Neoplastic Pathologic Fracture and Nonpathologic Fracture Using Hip Radiographs".

Using participatory action research to develop an artificial intelligence-augmented, peer-driven, case-based, and simulation-enhanced curriculum for emergency medicine residents.

This work describes the use of participatory action research to develop an artificial intelligence (AI)-augmented, peer-driven, case-based, and simula...

Uncovering the potential mechanisms of aspartame-induced oral leukoplakia via integrated network toxicology and molecular dynamics simulations.

OBJECTIVE: Aspartame, a widely used artificial sweetener, has attracted increasing attention for its potential systemic and oral toxicity. However, th...

Comparison of Artificial Intelligence Chatbots and Dental Students on Context of Dental Trauma.

AIM: Daily use of chatbots has become widespread in dental education just as in every other field. Dental students consult them when performing tasks ...

Artificial sweeteners and male infertility: Network toxicology and experimental evidence reveal FGFR1 as the critical target.

BACKGROUND: Artificial sweeteners (ASs) are widely used sugar substitutes, but chronic exposure is linked to male infertility. We integrated computati...

Artificial intelligence-driven triage and decision-making in trauma systems/settings.

PURPOSE OF REVIEW: Trauma care demands rapid decision-making under uncertainty and time pressure, where maintaining situational awareness becomes chal...

Building trust in the integration of artificial intelligence into chemical risk assessment: findings from the 2024 ECETOC workshop.

Artificial Intelligence (AI) is increasingly influencing chemical risk assessment, enabling faster, more comprehensive, and potentially more ethical a...

Diethyl Phthalate (DEP) as a potential osteosarcoma risk factor: a multi-omics study integrating network Toxicology, single-cell RNA sequencing, and molecular docking.

Diethyl phthalate (DEP), a common plasticiser and endocrine disruptor, has been linked to cancer, but its role in osteosarcoma (OS) remains unclear. T...

Exploring estrogen synthesis and metabolism related markers and immune dynamics in osteoporosis-insights from single-cell RNA sequencing and Mendelian randomization.

BACKGROUND: Osteoporosis (OP) is characterized by decreased bone mineral density and an increased fracture risk, often due to declining sex hormone le...