Emergency Medicine

Robotic operations in urgent general surgery: a systematic review.

Robotically assisted operations are the state of the art in laparoscopic general surgery. They are established predominantly for elective operations. Since laparoscopy is widely used in urgent general surgery, the significance of robotic assistance in urgent operations is of interest. Currently, there are few data on robotic-assisted operations in urgent surgery. The aim of this study was to colle...

New Robotic System with Wristed Microinstruments Allows Precise Reconstructive Microsurgery: Preclinical Study.

BACKGROUND: Microsurgery allows complex reconstruction of tissue defects after oncological resections or severe trauma. Performing these procedures may be limited by human tremor, precision, and manual dexterity. A new robot designed specifically for microsurgery with wristed microinstruments and motion scaling may reduce human tremor and thus enhance precision. This randomized controlled preclini...

Deep learning for emergency ascites diagnosis using ultrasonography images.

PURPOSE: The detection of abdominal free fluid or hemoperitoneum can provide critical information for clinical diagnosis and treatment, particularly i...

Deep-learning synthesized pseudo-CT for MR high-resolution pediatric cranial bone imaging (MR-HiPCB).

PURPOSE: CT is routinely used to detect cranial abnormalities in pediatric patients with head trauma or craniosynostosis. This study aimed to develop ...

Recognition and Segmentation of Individual Bone Fragments with a Deep Learning Approach in CT Scans of Complex Intertrochanteric Fractures: A Retrospective Study.

The characteristics of bone fragments are the main influencing factors for the choice of treatment in intertrochanteric fractures. This study aimed to...

FLeAC: A Human-Centered Associative Classifier Using the Validity Concept.

Fuzzy associative classifiers (FACs) have recently received considerable attention in the data mining community due to their ability to address the im...

Deep learning for behaviour classification in a preclinical brain injury model.

The early detection of traumatic brain injuries can directly impact the prognosis and survival of patients. Preceding attempts to automate the detecti...

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond.

The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target i...

Pilot Report for Intracranial Hemorrhage Detection with Deep Learning Implanted Head Computed Tomography Images at Emergency Department.

Hemorrhagic stroke is a serious clinical condition that requires timely diagnosis. An artificial intelligence algorithm system called DeepCT can ident...

Few-Shot Emergency Siren Detection.

It is a well-established practice to build a robust system for sound event detection by training supervised deep learning models on large datasets, bu...

Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning.

Cardiovascular diseases (CVD) are the leading cause of death worldwide. People affected by CVDs may go undiagnosed until the occurrence of a serious h...

Deep Learning-Based Prediction of Hematoma Expansion Using a Single Brain Computed Tomographic Slice in Patients With Spontaneous Intracerebral Hemorrhages.

OBJECTIVES: We aimed to predict hematoma expansion in intracerebral hemorrhage (ICH) patients by using the deep learning technique.

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications.

Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of a...

Intracerebral hemorrhage detection on computed tomography images using a residual neural network.

Intracerebral hemorrhage (ICH) is a high mortality rate, critical medical injury, produced by the rupture of a blood vessel of the vascular system ins...

Uncertainty-Aware Deep Learning With Cross-Task Supervision for PHE Segmentation on CT Images.

Perihematomal edema (PHE) volume, surrounding spontaneous intracerebral hemorrhage (SICH), is an important biomarker for the presence of SICH-associat...

Predicting early failure of quantum cascade lasers during accelerated burn-in testing using machine learning.

Device life time is a significant consideration in the cost of ownership of quantum cascade lasers (QCLs). The life time of QCLs beyond an initial bur...

Use of artificial intelligence in emergency radiology: An overview of current applications, challenges, and opportunities.

The value of artificial intelligence (AI) in healthcare has become evident, especially in the field of medical imaging. The accelerated pace and acuit...

Development of practical triage methods for critical trauma patients: machine-learning algorithm for evaluating hybrid operation theatre entry of trauma patients (THETA).

PURPOSE: Hybrid operating rooms benefit patients with severe trauma but have a prerequisite of significant resources. This paper proposes a practical ...

Analysis and Prevention and Control System of Domino Accident Risk Data in Chemical Parks Based on Topological Neural Network.

A topologically based neural network algorithm is used to conduct an in-depth study and analysis of domino accident risk data in chemical parks, and t...

Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture.

BACKGROUND: Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of pati...