Emergency Medicine

Artificial neural networks in contemporary toxicology research.

Artificial neural networks (ANNs) have a huge potential in toxicology research. They may be used to predict toxicity of various chemical compounds or classify the compounds based on their toxic effects. Today, numerous ANN models have been developed, some of which may be used to detect and possibly explain complex chemico-biological interactions. Fully connected multilayer perceptrons may in some ...

Cytologic scoring of equine exercise-induced pulmonary hemorrhage: Performance of human experts and a deep learning-based algorithm.

Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses, which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of annotators and to validate a deep learning-based algorithm for the THS. Digitized cytological s...

The synergy of synchrotron imaging and convolutional neural networks towards the detection of human micro-scale bone architecture and damage.

The growing health and economic burden of bone fractures, their intricate multiscale features and the existing knowledge gaps in the comprehension of ...

Deep learning approach for prediction of exergy and emission parameters of commercial high by-pass turbofan engines.

Aviation emissions originated from the fuel burn have been hot topics by engineers and policy-makers due to their harmful effects on the environment a...

Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients.

Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settings, deep learning-based models and especially conv...

Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis.

BACKGROUND: Subarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of morbidity and mortality. Early aneurysm identification, aided b...

Robot-patient registration for optical tracker-free robotic fracture reduction surgery.

BACKGROUND AND OBJECTIVE: Image-guided robotic surgery for fracture reduction is a medical procedure in which surgeons control a surgical robot to ali...

Key factors selection on adolescents with non-suicidal self-injury: A support vector machine based approach.

Comparing a family structure to a company, one can often think of parents as leaders and adolescents as employees. Stressful family environments and a...

Prediction of anemia using facial images and deep learning technology in the emergency department.

BACKGROUND: According to the WHO, anemia is a highly prevalent disease, especially for patients in the emergency department. The pathophysiological me...

Overtriage, Undertriage, and Value of Care after Major Surgery: An Automated, Explainable Deep Learning-Enabled Classification System.

BACKGROUND: In single-institution studies, overtriaging low-risk postoperative patients to ICUs has been associated with a low value of care; undertri...

A machine learning method for predicting the probability of MODS using only non-invasive parameters.

OBJECTIVES: Timely and accurate prediction of multiple organ dysfunction syndrome (MODS) is essential for the rescue and treatment of trauma patients ...

Robotic management of severe hemorrhage during para-aortic lymph node dissection.

Adaptive variable impedance position/force tracking control of fracture reduction robot.

BACKGROUND: The operation object of robot-assisted fracture reduction surgery is the musculoskeletal tissue with rigid-compliance coupling characteris...

Intelligent robot-assisted minimally invasive reduction system for reduction of unstable pelvic fractures.

OBJECTIVE: Currently, minimally invasive internal fixation is recommended for the surgical treatment of unstable pelvic fractures. The premise and dif...

Intelligent injury prediction for traumatic airway obstruction.

Airway obstruction is one of the crucial causes of death in trauma patients during the first aid. It is extremely challenging to accurately treat a gr...

Predicting prognosis of primary pontine hemorrhage using CT image and deep learning.

Prognosis of primary pontine hemorrhage (PPH) is important for treatment planning and patient management. However, only few clinical factors were repo...

A novel application of neural networks to identify potentially effective combinations of biologic factors for enhancement of bone fusion/repair.

INTRODUCTION: The use of biologic adjuvants (orthobiologics) is becoming commonplace in orthopaedic surgery. Among other applications, biologics are o...

A Natural Language Processing and Machine Learning Approach to Identification of Incidental Radiology Findings in Trauma Patients Discharged from the Emergency Department.

STUDY OBJECTIVE: Patients undergoing diagnostic imaging studies in the emergency department (ED) commonly have incidental findings, which may represen...

Progress in Control-Actuation Robotic System for Gastrointestinal NOTES Development.

PURPOSE: Natural orifice transluminal endoscopic surgery (NOTES) is a minimally invasive surgical procedure that reduces patient trauma, infection pro...

Artificial intelligence fracture recognition on computed tomography: review of literature and recommendations.

PURPOSE: The use of computed tomography (CT) in fractures is time consuming, challenging and suffers from poor inter-surgeon reliability. Convolutiona...