Emergency Medicine

The feasibility of deep learning-based synthetic contrast-enhanced CT from nonenhanced CT in emergency department patients with acute abdominal pain.

Our objective was to investigate the feasibility of deep learning-based synthetic contrast-enhanced CT (DL-SCE-CT) from nonenhanced CT (NECT) in patients who visited the emergency department (ED) with acute abdominal pain (AAP). We trained an algorithm generating DL-SCE-CT using NECT with paired precontrast/postcontrast images. For clinical application, 353 patients from three institutions who vis...

Machine learning-assisted screening for cognitive impairment in the emergency department.

BACKGROUND/OBJECTIVES: Despite a high prevalence and association with poor outcomes, screening to identify cognitive impairment (CI) in the emergency department (ED) is uncommon. Identification of high-risk subsets of older adults is a critical challenge to expanding screening programs. We developed and evaluated an automated screening tool to identify a subset of patients at high risk for CI.

Thoracolumbar Burst Fractures: A Systematic Review and Meta-Analysis on the Anterior and Posterior Approaches.

BACKGROUND: A thoracolumbar burst fracture (BF) is a severe type of compression fracture, which is the most common type of traumatic spine fractures. ...

Validation of an artificial intelligence solution for acute triage and rule-out normal of non-contrast CT head scans.

PURPOSE: Non-contrast CT head scans provide rapid and accurate diagnosis of acute head injury; however, increased utilisation of CT head scans makes i...

Improved Prediction of Older Adult Discharge After Trauma Using a Novel Machine Learning Paradigm.

BACKGROUND: The ability to reliably predict outcomes after trauma in older adults (age ≥ 65 y) is critical for clinical decision making. Using novel m...

A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department.

BACKGROUND: Currently, the risk stratification of critically ill patient with chest pain is a challenge. We aimed to use machine learning approach to ...

Ultrasound Images Guided under Deep Learning in the Anesthesia Effect of the Regional Nerve Block on Scapular Fracture Surgery.

In order to discuss the clinical characteristics of patients with scapular fracture, deep learning model was adopted in ultrasound images of patients ...

Machine learning and artificial intelligence: applications in healthcare epidemiology.

Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a ...

Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure-Activity Relationship System.

In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vi...

Deep Learning for Automated Triaging of 4581 Breast MRI Examinations from the DENSE Trial.

Background Supplemental screening with MRI has proved beneficial in women with extremely dense breasts. Most MRI examinations show normal anatomic and...

A deep learning-based model for prediction of hemorrhagic transformation after stroke.

Hemorrhagic transformation (HT) is one of the most serious complications after endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) patients...

A Robust Deep Learning Segmentation Method for Hematoma Volumetric Detection in Intracerebral Hemorrhage.

BACKGROUND AND PURPOSE: Hematoma volume (HV) is a significant diagnosis for determining the clinical stage and therapeutic approach for intracerebral ...

Spatialising urban health vulnerability: An analysisof NYC's critical infrastructure during COVID-19.

This paper examines how fragmentation of critical infrastructure impacts the spread of the coronavirus outbreak in New York City at the neighbourhood ...

Deep Learning to Decipher the Progression and Morphology of Axonal Degeneration.

Axonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to un...

CheXED: Comparison of a Deep Learning Model to a Clinical Decision Support System for Pneumonia in the Emergency Department.

PURPOSE: Patients with pneumonia often present to the emergency department (ED) and require prompt diagnosis and treatment. Clinical decision support ...

Acute toxicity of aqueous extract of Mill. on biochemical and histopathological parameters in rats.

Medicinal plants play an important role in the management of various diseases, so their use has become widespread. However, in some cases the populati...

Cefepime Induced Neurotoxicity Following A Regimen Dose-Adjusted for Renal Function: Case Report and Review of the Literature.

Cefepime induced neurotoxicity (CIN) is commonly associated with renal dysfunction, however CIN can occur in patients with normal renal function or r...

Artificial Intelligence Trained by Deep Learning Can Improve Computed Tomography Diagnosis of Nontraumatic Subarachnoid Hemorrhage by Nonspecialists.

Subarachnoid hemorrhage (SAH) is a serious cerebrovascular disease with a high mortality rate and is known as a disease that is hard to diagnose becau...

Multi-Class brain normality and abnormality diagnosis using modified Faster R-CNN.

BACKGROUND AND OBJECTIVE: The detection and analysis of brain disorders through medical imaging techniques are extremely important to get treatment on...

Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning.

Dual-energy X-ray absorptiometry (DXA) is underutilized to measure bone mineral density (BMD) and evaluate fracture risk. We present an automated tool...