Emergency Medicine

Deep learning advances high-throughput toxicity screening of chemicals at multi-biological levels.

Deep learning (DL) has emerged as a powerful tool for modeling unstructured data, thereby improving prediction accuracy and expanding the application of machine learning (ML) in toxicity assessment. However, selecting suitable DL architectures and training methods for toxicity prediction remains challenging due to the lack of systematic comparisons regarding data types and modeling tasks across bi...

Septic shock: Past, present, and perspectives.

Septic shock remains one of the most severe complications of infection, defined by circulatory, cellular, and metabolic dysfunction and associated with persistently high mortality. The concept has evolved markedly, from early descriptions of "blood poisoning" to the recognition of sepsis as a systemic syndrome in the late 20th century. Consensus definitions and large clinical trials, including ear...

Current State-of-the-Art 3D MRI Sequences for Assessing Bone Morphology with Emphasis on Cranial and Spinal Imaging: A Narrative Review.

Traditionally, CT has been the go-to method for visualizing bone structures, while MRI has been preferred for assessing soft tissues, because structur...

Smart Hospital Infrastructure: Enhancing Parking Management and Ambulance Accessibility in Urban Healthcare Systems.

Hospitals face significant challenges in parking management and assessing ambulances due to fast-growing urbanization, high population density, and tr...

Elucidating anti-triple-negative breast cancer mechanisms and mitigating toxicity of Fritillaria thunbergii Miq.: A multi-omics and network pharmacology approach.

ETHNOPHARMACOLOGICAL RELEVANCE: Fritillaria thunbergii Miq. (Zhebeimu, ZBM) is traditionally recognized in Chinese medicine for its effects of clearin...

Toward Standardized ICU Trauma Resuscitation via Markov Decision Processes.

OBJECTIVE: Guideline-based recommendations for posthemostasis resuscitation in trauma patients remain limited. This study aimed to define an interpret...

Comparison of the Diagnostic Utility of Computed Tomography Angiography Head With and Without 3-Dimensional Volume-Rendered Images for Aneurysm Detection in Subarachnoid Hemorrhage Patients Versus Digital Subtraction Angiography.

BACKGROUND AND OBJECTIVES: Generating computed tomography (CT) angiography (CTA) 3-dimensional (3D) volume-rendered (3DVR) images can be time consumin...

An interpretable machine learning approach to evaluate 30-day mortality risk in patients with community-onset bacteremia.

BACKGROUND: Machine learning (ML) techniques are increasingly being used in health outcome research to develop predictive models. However, ML models a...

Identification of key genes in intracranial aneurysm via WGCNA: insights into immune microenvironment and therapeutic targets.

BACKGROUND: Intracranial aneurysms (IA) are prevalent vascular lesions whose rupture causes subarachnoid hemorrhage with high disability and mortality...

Evaluating the diagnostic accuracy of AI in ischemic and hemorrhagic stroke: A comprehensive meta-analysis.

Stroke poses a significant health challenge, with ischemic and hemorrhagic subtypes requiring timely and accurate diagnosis for effective management. ...

Are Artificial Intelligence Models Reliable for Clinical Application in Pediatric Fracture Detection on Radiographs? A Systematic Review and Meta-analysis.

BACKGROUND: Artificial intelligence (AI) applications for pediatric fracture diagnosis using radiographs have demonstrated growing potential in clinic...

Machine Learning in Venous Thromboembolism-Why and What Next?

Venous thromboembolism (VTE) remains a leading cause of cardiovascular morbidity and mortality, despite advances in imaging and anticoagulation. VTE a...

Using artificial intelligence as a technological tool in gynecologic and obstetric health: A narrative literature review.

Maternal mortality remains a critical global public health issue, particularly in low- and middle-income settings where failures in surveillance, earl...

Predicting fracture toughness of human cortical bone from donors with and without type 2 diabetes using Raman spectroscopy and machine learning.

Type 2 diabetes mellitus (T2DM) is associated with increased skeletal fragility, yet standard clinical assessments often fail to detect diabetes-induc...

Graph Neural Networks for Realistic Bleeding Prediction in Surgical Simulators.

This study presents a novel approach using graph neural networks to predict the risk of internal bleeding using vessel maps derived from patient CT an...

From prediction to precision: The emerging impact of artificial intelligence in trauma care.

A Dynamic Machine Learning Model to Predict Angiographic Vasospasm After Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND AND OBJECTIVES: The goal of this study was to develop a highly precise, dynamic machine learning model centered on daily transcranial Doppl...

Endodontic diagnostics training in undergraduate dental education: An observational pilot study on AI-driven virtual patient e-learning.

AIM: To develop and evaluate an e-learning tool utilizing a generative pre-trained transformer (GPT), a form of artificial intelligence (AI), to allow...

Beyond Adverse Childhood Experiences: What Should be Considered for Trauma-Focused Adolescent Mental Health Risk Assessments?

To align with emerging policies for adolescents, feasible, accurate, and equitable trauma-focused assessment protocols need to be developed. To date, ...

Diagnostic technologies for neuroblastoma.

Neuroblastoma is an aggressive childhood cancer characterised by high relapse rates and heterogenicity. Current medical diagnostic methods involve an ...