Emergency Medicine

Ultrasound to guide critical decisions: What you need to know.

BACKGROUND: Hypotension in the intensive care unit (ICU) demands rapid diagnosis and intervention, as delays in identifying the etiology of shock directly impact patient outcomes. Point-of-care ultrasound (POCUS) has become an indispensable tool for intensivists and acute care surgeons, enabling bedside classification of shock physiology and guiding targeted resuscitation. METHODS: This review syn...

An explainable machine learning model for comorbidity risk stratification in patients with fractures admitted to the intensive care unit: a multicenter study.

BACKGROUND: Among traumatic-fracture patients admitted to intensive care units (ICUs), those with substantial chronic comorbidities recover more slowly and die more often than their counterparts without such conditions. The age-adjusted Charlson Comorbidity Index (aCCI) quantifies this burden, yet clinicians still lack a tool that can identify-at the point of ICU admission-which fracture patients ...

Removal of a quaternary ammonium compound by electrocoagulation: Mechanistic analysis and multi-response optimization using response surface methodology and machine learning.

The widespread use of quaternary ammonium compounds (QACs), intensified by the COVID-19 pandemic, has led to their increasing presence in aquatic envi...

Patients visits forecasting in emergency departments: Self-adapting LSTM models for evolving data distributions.

BACKGROUND AND OBJECTIVE: Overcrowding in emergency departments (EDs) remains a critical challenge in healthcare systems. Accurate forecasting of pati...

Deep learning framework for bronchoscopic diagnosis of burn inhalation injury.

Burn inhalation injury (BII) increases mortality and morbidity in burns patients. Accurate bronchoscopic grading, as the gold standard diagnostic moda...

Digital Eye: Deep Learning for Detecting Physeal Fractures of the Pediatric Distal Radius.

BACKGROUND: Distal radial fractures (DRFs) are some of the most common pediatric injuries, often involving the physis. Diagnostic accuracy can be chal...

Development and Deployment of a Machine Learning Model to Triage the Use of Prostate MRI (ProMT-ML) in Patients With Suspected Prostate Cancer.

BACKGROUND: Access to prostate MRI remains limited due to resource constraints and the need for expert interpretation. PURPOSE: To develop machine lea...

Precision medicine in orthopaedics: A review of current technologies and future directions.

PURPOSE: To synthesise the paradigm shift towards precision medicine in orthopaedics, where individual anatomical, biomechanical, molecular and kinema...

From static to robotic: evolving navigation systems in oral and maxillofacial surgery.

Navigation-assisted surgical systems in oral and maxillofacial surgery have evolved considerably over the past 5 years, with newer modifications aimed...

Artificial Intelligence Can Inform Prediction of American Society of Anesthesiologists Physical Status Classification in Hand Surgery.

BACKGROUND: Hand surgeons are increasingly asked to assign American Society of Anesthesiologists (ASA) physical status classifications for local anest...

An Interpretable Machine Learning Model for Early Multitemporal Prediction of Onset of Acute Kidney Injury in Intensive Care Unit Patients with Severe Trauma.

Background: Acute Kidney Injury (AKI), a leading organ failure cause in critical patients, demands early high-risk identification to enhance outcomes....

Hemodynamic phenotyping 4.0.

The concept of integrating hemodynamic variables to define specific profiles or phenotypes has been established for decades. Describing hemodynamic ph...

4-Nonylphenol mediated tumor progression and immune modulation through CDK2: Computational and experimental validation.

As an endocrine-disrupting chemical, 4-nonylphenol (4-NP) has been found above safe limits in global water systems and associated with cancer progress...

Machine learning-based prediction of postpartum hemorrhage using maternal clinical and biochemical features.

OBJECTIVE: This study evaluates the predictive performance of various machine learning (ML) algorithms for postpartum hemorrhage (PPH), peripartum hys...

Multi-modal deep-learning troponin prediction from electrocardiograms and demographic data.

AIMS: Electrocardiograms (ECGs) and troponin (Tn) testing are essential tools for the diagnosis and management of cardiac conditions. Prompt diagnosis...

Early prediction of septic shock in ICU patients using machine learning: development, external validation, and explainability with SHAP.

BACKGROUND: Septic shock is a severe and life-threatening complication of sepsis associated with high mortality. Early identification remains challeng...

Swin-EffuseNet: A dual-stream attention-based model combining Swin transformer V2 and EfficientNet-BO for bone fracture classification.

CONTEXT: Bone fractures are among the most common musculoskeletal injuries and require timely, accurate diagnosis to ensure effective treatment and pr...

In silico prediction of endocrine activity.

Endocrine-disrupting chemicals (EDCs) pose health risks; yet, conventional in vitro and in vivo testing remains slow, costly, and animal-intensive. En...

Ergonomic handheld chip: An instrument-free RPA-CRISPR platform for rapid home self-testing.

Global outbreaks of viral infectious diseases underscore the urgent need for low-cost, instrument-free, and user-friendly molecular testing platforms ...

The effect of AI assistance timing on performance and user perceptions in pathological slide diagnosis.

This study evaluated three timing strategies for delivering AI assistance in pathological slide diagnosis - pre-diagnosis (triage), during diagnosis (...