Emergency Medicine

Research on predicting hematoma expansion in spontaneous intracerebral hemorrhage based on deep features of the VGG-19 network.

PURPOSE: To construct a clinical noncontrastive computed tomography (NCCT) deep learning joint model for predicting early hematoma expansion (HE) after cerebral hemorrhage (sICH) and evaluate its predictive performance.

An efficient pipeline to compute patient-specific cerebral aneurysm wall tension

Cerebral aneurysm rupture, leading to subarachnoid hemorrhage with a high mortality rate, disproportionately affects younger populations, resulting in a significant loss of productive life years. A significant proportion of these deaths is due to aneurysmal re-bleeding within the first three days following the initial bleed, prior to treatment. While early aneurysm treatment is recommended, ther...

Calorie Burn Estimation in Community Parks Through DLICP: A Mathematical Modelling Approach

Community parks play a crucial role in promoting physical activity and overall well-being. This study introduces DLICP (Deep Learning Integrated Com...

ProTox 3.0: a webserver for the prediction of toxicity of chemicals.

Interaction with chemicals, present in drugs, food, environments, and consumer goods, is an integral part of our everyday life. However, depending on ...

Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis

We applied machine learning to the unmet medical need of rapid and accurate diagnosis and prognosis of acute infections and sepsis in emergency depa...

Prediction of Occult Hemorrhage in the Lower Body Negative Pressure Model: Initial Validation of Machine Learning Approaches.

INTRODUCTION: Detection of occult hemorrhage (OH) before progression to clinically apparent changes in vital signs remains an important clinical probl...

DP2LM: leveraging deep learning approach for estimation and hypothesis testing on mediation effects with high-dimensional mediators and complex confounders.

Traditional linear mediation analysis has inherent limitations when it comes to handling high-dimensional mediators. Particularly, accurately estimati...

Performance of an Open-Source Large Language Model in Extracting Information from Free-Text Radiology Reports.

Purpose To assess the performance of a local open-source large language model (LLM) in various information extraction tasks from real-life emergency b...

Improving Automated Hemorrhage Detection at Sparse-View CT via U-Net-based Artifact Reduction.

Purpose To explore the potential benefits of deep learning-based artifact reduction in sparse-view cranial CT scans and its impact on automated hemorr...

A Three-Stage Semi-Supervised Learning Approach to Spine Image Segmentation.

Spine segmentation in computed tomography (CT) images is critical for automatic analysis, especially when focusing on varied spinal anatomy. Despite h...

ECG-based Daily Activity Recognition Using 1D Convolutional Neural Networks.

This study presents an approach to human activity recognition (HAR) using electrocardiogram (ECG) signals. We explore the application of ECG for not o...

Predicting Hemodynamic and Pulmonary Decompensation with Deep Neural Networks: Performance and Explainability.

Predicting the deterioration of patients' hemodynamic and pulmonary decompensation state while being treated on an intensive care unit is demanding fo...

Improving Neonatal Care with AI: Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants.

In this study, we developed an AI model to predict Respiratory Distress Syndrome (RDS) in premature infants, aiming to reduce unnecessary treatment wi...

Embedded Force Sensor with Deep Transformation Calibration for Interventional Soft Robots.

A calibration method for gelatin-graphite-based soft sensors is proposed. This approach uses convolutional deep learning approaches that account for a...

SimICL: A Simple Visual In-context Learning Framework for Ultrasound Segmentation.

Conventional deep learning models deal with images one-by-one, requiring costly and time-consuming expert labeling in the field of medical imaging, an...

Acute Stress Disorder Detection using Machine Learning based on resting-state fMRI.

Early diagnosis of Acute Stress Disorder (ASD) is important, given its potential progression to post-traumatic system disorder (PTSD). The current dia...

Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning.

AIMS: Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the USA with morbidity and mortality...

VR-NRP: A Virtual Reality Simulation for Training in the Neonatal Resuscitation Program

The use of Virtual Reality (VR) technologies has been extensively researched in surgical and anatomical education. VR provides a lifelike and intera...

Automatic diagnosis of pediatric supracondylar humerus fractures using radiomics-based machine learning.

The aim of this study was to construct a classification model for the automatic diagnosis of pediatric supracondylar humerus fractures using radiomics...

Prediction of gait recovery using machine learning algorithms in patients with spinal cord injury.

With advances in artificial intelligence, machine learning (ML) has been widely applied to predict functional outcomes in clinical medicine. However, ...