Emergency Medicine

Exploring an Intermediate Colorectal Cancer Screening Test Based on Stool Proteomics and Machine Learning for Optimizing the Selection of Patients for Colonoscopy Identified from FIT.

The fecal immunochemical test (FIT) for detecting fecal occult blood, used alone or in combination w...

Multi-level evidence reveals KCTD15 and FAM19A2 as key targets in PFOA/PFOS-mediated PCOS pathogenesis.

BACKGROUND: Polycystic ovary syndrome (PCOS), a common endocrine disorder, has been linked to enviro...

Automated Non-Invasive Burn Diagnostic System for Healthcare using Artificial Intelligence: AMBUSH-AI.

OBJECTIVE: Develop technology to predict burn wound depth using a combination of FDA approved ultras...

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

BACKGROUND: Stroke is a complex, multidimensional disorder influenced by interacting inflammatory, i...

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, contribu...

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 carcinoge...

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 undersuppl...

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 tomogra...

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 maj...

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...

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 Marshal...

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 (...

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...

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. D...

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 l...

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, wh...

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, ...

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, bu...

Artificial intelligence meets point-of-care ultrasound: implications for pediatric emergency and critical care.

PURPOSE OF REVIEW: Artificial intelligence has been integrated in nearly all aspects of clinical car...