Emergency Medicine

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 arises from diverse and overlapping risk factors, such as inherited thrombophilia, immobility, malignancy, surgery or trauma, pregnancy, hormonal therapy, obesity, chronic medical conditions (e.g., heart failure, inflammatory disease), and advancing 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, early diagnosis, and clinical decision making compromise obstetric care. In this context, the present study aimed to critically review the scientific literature on the use of artificial intelligence (AI) in gynecologic and obstetric health, focusing on t...

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 ...

The association between cerebral small vessel disease and unfavorable hematoma morphology in primary intracerebral hemorrhage.

OBJECTIVE: To study the association between cerebral small vessel diseases (CSVD) and unfavorable hematoma morphology in primary intracerebral hemorrh...

Prediction of gastrointestinal hemorrhage in cardiology inpatients using an interpretable XGBoost model.

Gastrointestinal bleeding (GIB) occurs more frequently in cardiovascular patients than in the general population, significantly affecting morbidity an...

Current state of AI for shockwave lithotripsy: a systematic review from YAU and EAU endourology.

PURPOSE: To consolidate the current evidence of artificial intelligence (AI) for management of nephrolithiasis using extracorporeal shock-wave lithotr...

Rapid trauma classification under data scarcity: an emergency on-scene decision model combining natural language processing and machine learning.

Trauma has become a major cause of increased morbidity and mortality worldwide. In emergency response, the classification of injuries is crucial as it...

Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes.

Accurately predicting the severity of subarachnoid hemorrhage (SAH) is critical for informing clinical decisions and improving patient outcomes. This ...

Acute Management of Nasal Bone Fractures: A Systematic Review and Practice Management Guideline.

Nasal bone fractures represent the most common facial skeletal injury, challenging both function and aesthetics. This Preferred Reporting Items for Sy...

Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage.

Lower extremity deep vein thrombosis is one of the important complications of spontaneous intracerebral hemorrhage. We aimed to develop a risk assessm...

Human and animal models for studying hemorrhagic shock.

BACKGROUND: Studying the physiological response to severe hemorrhage remains challenging in real-world patients. Animal and human models mitigate some...

Humanizing AI training for health professions educators.

WHAT WAS THE EDUCATIONAL CHALLENGE?: Health professions educators grapple with profound emotional burdens-technology-related distress, gnawing self-do...

Machine learning combined with the PMF model reveals the sources and driving factors of PAHs and Cl-PAHs in urban runoff.

Urban rainwater runoff is an important source of nonpoint source pollution due to its transport of diverse contaminants, including polycyclic aromatic...

Assessing the Accuracy of ChatGPT in Appropriately Triaging Common Postoperative Concerns Regarding Mohs Micrographic Surgery.

Artificial intelligence (AI) is increasingly integrated into health care, offering potential benefits in patient education, triage, and administrative...