Emergency Medicine

Using Explainable Artificial Intelligence Models (ML) to Predict Suspected Diagnoses as Clinical Decision Support.

The complexity of emergency cases and the number of emergency patients have increased dramatically. Due to a reduced or even missing specialist medical staff in the emergency departments (EDs), medical knowledge is often used without professional supervision for the diagnosis. The result is a failure in diagnosis and treatment, even death in the worst case. Secondary: high expenditure of time and ...

Deep Learning-Based Brain Hemorrhage Detection in CT Reports.

Radiology reports can potentially be used to detect critical cases that need immediate attention from physicians. We focus on detecting Brain Hemorrhage from Computed Tomography (CT) reports. We train a deep learning classifier and observe the effect of using different pre-trained word representations along with domain-specific fine-tuning. We have several contributions. Firstly, we report the res...

[Arthroscopic-assisted fracture reduction combined with robot-navigated nail placement to treat talar neck fractures].

This study aims to explore the clinical efficacy of arthroscopic-assisted reduction combined with robot-navigated nail placement in the treatment of t...

Towards the Application of Machine Learning in Emergency Informatics.

Emergency care is one of the cornerstone parts of the world health organization's action plan. Rapid response and immediate care are considered in agi...

Robot-Assisted Surgery for Endometriosis Current and Future Perspectives.

Successful resection of all visible lesions may effectively treat endometriosis-related infertility and pelvic pain. Minimally invasive surgery provid...

Potential for Machine Learning in Burn Care.

Burn-related injuries are a leading cause of morbidity across the globe. Accurate assessment and treatment have been demonstrated to reduce the morbid...

Mass Deployment of Deep Neural Network: Real-Time Proof of Concept With Screening of Intracranial Hemorrhage Using an Open Data Set.

BACKGROUND: Intracranial hemorrhage (ICH) is considered an emergency that requires rapid medical or surgical management. Previous studies have used ar...

Death by Robots? Automation and Working-Age Mortality in the United States.

The decline of manufacturing employment is frequently invoked as a key cause of worsening U.S. population health trends, including rising mortality du...

Utilizing human intelligence in artificial intelligence for detecting glaucomatous fundus images using human-in-the-loop machine learning.

PURPOSE: For diagnosing glaucomatous damage, we have employed a novel convolutional neural network (CNN) from TrueColor confocal fundus images to conq...

Tox-GAN: An Artificial Intelligence Approach Alternative to Animal Studies-A Case Study With Toxicogenomics.

Animal studies are a critical component in biomedical research, pharmaceutical product development, and regulatory submissions. There is a worldwide e...

The derivation of an International Classification of Diseases, Tenth Revision-based trauma-related mortality model using machine learning.

BACKGROUND: Existing mortality prediction models have attempted to quantify injury burden following trauma-related admissions with the most notable be...

Road traffic accident trauma: A model for road safety management utilizing the artificial intelligence with geo-mapping and geospatial data in Pakistan.

A systemic review and meta-analysis of the effects of perioperative anticoagulant and antiplatelet therapy on bleeding complications in robot-assisted prostatectomy.

OBJECTIVE: Robot-assisted prostatectomy is commonly performed for the management of prostate cancer. The literature has noted that prostate cancer pat...

[Application of spinal surgical robots in minimally invasive surgery for thoracolumbar fracture].

OBJECTIVE: To evaluate the safety and accuracy of Renaissance robot navigation system in minimally invasive surgery for thoracolumbar fracture.

[Management of the nondisplaced type Herbert D1 scaphoid fracture with robot navigation combined with wrist arthroscopy].

To investigate the feasibility and the clinical efficiency of robot navigation combined with wrist arthroscopy in minimally invasive treatment of nond...

Invited Commentary on: Artificial Neural Networks Predict 30-Day Mortality After Hip Fracture: Insights From Machine Learning.

Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest.

OBJECTIVES: Prognostication of neurologic status among survivors of in-hospital cardiac arrests remains a challenging task for physicians. Although mo...

Decision support model for the patient admission scheduling problem based on picture fuzzy aggregation information and TOPSIS methodology.

Health care systems around the world do not have sufficient medical services to immediately offer elective (e.g., scheduled or non-emergency) services...

Scan-less machine-learning-enabled incoherent microscopy for minimally-invasive deep-brain imaging.

Deep-brain microscopy is strongly limited by the size of the imaging probe, both in terms of achievable resolution and potential trauma due to surgery...

Validation of a Machine Learning Model for Early Shock Detection.

OBJECTIVES: The objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (...