Emergency Medicine

Toxicovigilance 2.0 - modern approaches for the hazard identification and risk assessment of toxicants in human beings: A review.

The attempt to define toxicovigilance can be based on defining its fundamental principles: prevention of infections with toxic substances, collecting information on poisonings, both in terms of their sources and side effects, and confirming poisonings, with the aim of improving treatment. Substances referred to include both those originating from animal bites, ingested inadvertently, and those res...

Examining arterial pulsation to identify and risk-stratify heart failure subjects with deep neural network.

Hemodynamic parameters derived from pulse wave analysis have been shown to predict long-term outcomes in patients with heart failure (HF). Here we aimed to develop a deep-learning based algorithm that incorporates pressure waveforms for the identification and risk stratification of patients with HF. The first study, with a case-control study design to address data imbalance issue, included 431 sub...

Exploring the role of large language models in radiation emergency response.

In recent times, the field of artificial intelligence (AI) has been transformed by the introduction of large language models (LLMs). These models, pop...

Both coiling and clipping induce the time-dependent release of endogenous neuropeptide Y into serum.

BACKGROUND: The vaso- and psychoactive endogenous Neuropeptide Y (NPY) has repeatedly been shown to be excessively released after subarachnoid hemorrh...

Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT.

PURPOSE: To predict hematoma growth in intracerebral hemorrhage patients by combining clinical findings with non-contrast CT imaging features analyzed...

A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department.

This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification t...

In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches.

The accurate identification of chemicals with ocular toxicity is of paramount importance in health hazard assessment. In contemporary chemical toxicol...

Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance.

This study aimed to assess the performance of a deep learning algorithm in helping radiologist achieve improved efficiency and accuracy in chest radio...

Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques.

BACKGROUND: The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typical...

MCPNET: Development of an interpretable deep learning model based on multiple conformations of the compound for predicting developmental toxicity.

The development of deep learning models for predicting toxicological endpoints has shown great promise, but one of the challenges in the field is the ...

Evaluation of a Novel Veterinary Dental Radiography Artificial Intelligence Software Program.

There is a growing trend of artificial intelligence (AI) applications in veterinary medicine, with the potential to assist veterinarians in clinical d...

Machine learning models for predicting unscheduled return visits to an emergency department: a scoping review.

BACKGROUND: Unscheduled return visits (URVs) to emergency departments (EDs) are used to assess the quality of care in EDs. Machine learning (ML) model...

Assessment of Color Reproducibility and Mitigation of Color Variation in Whole Slide Image Scanners for Toxicologic Pathology.

Digital pathology workflows in toxicologic pathology rely on whole slide images (WSIs) from histopathology slides. Inconsistent color reproduction by ...

Deep learning-based white matter lesion volume on CT is associated with outcome after acute ischemic stroke.

BACKGROUND: Intravenous thrombolysis (IVT) before endovascular treatment (EVT) for acute ischemic stroke might induce intracerebral hemorrhages which ...

Nonradiology Health Care Professionals Significantly Benefit From AI Assistance in Emergency-Related Chest Radiography Interpretation.

BACKGROUND: Chest radiographs (CXRs) are still of crucial importance in primary diagnostics, but their interpretation poses difficulties at times.

Deep learning for real-time multi-class segmentation of artefacts in lung ultrasound.

Lung ultrasound (LUS) has emerged as a safe and cost-effective modality for assessing lung health, particularly during the COVID-19 pandemic. However,...

Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept.

PURPOSE: Early administration and protocolization of massive hemorrhage protocols (MHP) has been associated with decreases in mortality, multiorgan sy...

[Development of prognostic clinical and genetic models of the risk of low bone mineral density using neural network training].

BACKGROUND: Osteoporosis is a common age-related disease with disabling consequences, the early diagnosis of which is difficult due to its long and hi...

AI-based X-ray fracture analysis of the distal radius: accuracy between representative classification, detection and segmentation deep learning models for clinical practice.

OBJECTIVES: To aid in selecting the optimal artificial intelligence (AI) solution for clinical application, we directly compared performances of selec...

Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department.

BACKGROUND: Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, thes...