Emergency Medicine

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 learning (ML) models that enable risk-based triage for prostate MRI (ProMT-ML) in the evaluation of prostate cancer. STUDY TYPE: Retrospective and prospective. POPULATION: A total of 11,879 retrospective MRI scans for suspected prostate cancer from a mu...

LLM-based pedagogical agent for ICU simulation instructor training: A quasi-experimental study.

BACKGROUND: Intensive Care Unit (ICU) nursing is demanding, requiring advanced clinical decision-making and emergency management skills. Simulation-based instruction is central to ICU nursing education but remains constrained by the cost and time required for scenario authoring, limited faculty capacity for feedback, and slow content updates. Large language models (LLMs)-based pedagogical agents m...

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...

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...

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 (...

Distributed Precision Stroke Care: Artificial Intelligence-Driven Stroke Management Using Multimodal Sensor Data.

Delays in stroke diagnosis contribute to long-term disability. Many patients still face barriers to effective risk factor management, timely detection...

Augmenting decision making in acute care surgery: A systematic review of machine learning-driven risk prediction models.

BACKGROUND: Acute care surgery (ACS) involves rapid, high-stakes decisions with limited opportunity for preoperative planning. While machine learning ...

Performance of machine learning algorithms in predicting the need for surgical fixation in pediatric craniomaxillofacial trauma.

Machine learning offers a novel approach to improve surgical triage in pediatric craniomaxillofacial trauma, where decision-making often relies on cli...

Evaluating bone density with ultrasonic backscatter: Leveraging time-frequency analyses and convolutional neural networks.

Osteoporosis is a disease characterized by decreased bone density and increased fracture risk. This study proposes a convolutional neural network (CNN...

Sysmex Cell Population Data for Diagnosing Infection in Patients With Suspected Sepsis in the Emergency Department.

OBJECTIVES: Early diagnosis of suspected sepsis is crucial to improve patient survival. Cell population (CP) data, a set of leucocyte research paramet...

CEVG-RTNet: A real-time architecture for robust forest fire smoke detection in complex environments.

Forest fire smoke detection is crucial for early warning and emergency management, especially under complex environmental conditions such as low contr...

The Performance of Artificial Intelligence in Providing Real-Time Aid in Emergency Dental Trauma: A Clinical Validation Study.

BACKGROUND: Searching online for dental emergency treatment as a non-expert can lead to unreliable guidance. We tested the publicly available first mu...