Emergency Medicine

Deep learning approach to assess damage mechanics of bone tissue.

Machine learning methods have the potential to transform imaging techniques and analysis for healthcare applications with automation, making diagnostics and treatment more accurate and efficient, as well as to provide mechanistic insights into tissue deformation and fracture in physiological and pathological conditions. Here we report an exploratory investigation for the classification and predict...

Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement.

BACKGROUND: Accurate measurement of hemorrhage volume is critical for both the prediction of prognosis and the selection of appropriate clinical treatment after spontaneous intracerebral hemorrhage (ICH). This study aimed to evaluate the performance and accuracy of a deep learning-based automated segmentation algorithm in segmenting spontaneous intracerebral hemorrhage (ICH) volume either with or ...

A deep learning approach to automate whole-genome prediction of diverse epigenomic modifications in plants.

Epigenetic modifications function in gene transcription, RNA metabolism, and other biological processes. However, multiple factors currently limit the...

External Validation of Deep Learning Algorithm for Detecting and Visualizing Femoral Neck Fracture Including Displaced and Non-displaced Fracture on Plain X-ray.

This study aimed to develop a method for detection of femoral neck fracture (FNF) including displaced and non-displaced fractures using convolutional ...

A Machine Learning Approach in Predicting Mortality Following Emergency General Surgery.

BACKGROUND: There is a significant mortality burden associated with emergency general surgery (EGS) procedures. The objective of this study was to dev...

A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans.

Acute Intracranial hemorrhage (ICH) is a life-threatening disease that requires emergency medical attention, which is routinely diagnosed using non-co...

Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification.

The Covid-19 European outbreak in February 2020 has challenged the world's health systems, eliciting an urgent need for effective and highly reliable ...

Present Limitations of Artificial Intelligence in the Emergency Setting - Performance Study of a Commercial, Computer-Aided Detection Algorithm for Pulmonary Embolism.

PURPOSE:  Since artificial intelligence is transitioning from an experimental stage to clinical implementation, the aim of our study was to evaluate t...

A deep-learning method using computed tomography scout images for estimating patient body weight.

Body weight is an indispensable parameter for determination of contrast medium dose, appropriate drug dosing, or management of radiation dose. However...

Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions.

IMPORTANCE: Triage in the emergency department (ED) is a complex clinical judgment based on the tacit understanding of the patient's likelihood of sur...

Documentation of Shared Decisionmaking in the Emergency Department.

STUDY OBJECTIVE: While patient-centered communication and shared decisionmaking are increasingly recognized as vital aspects of clinical practice, lit...

Beyond motor recovery after stroke: The role of hand robotic rehabilitation plus virtual reality in improving cognitive function.

Robot-assisted hand training adopting end-effector devices results in an additional reduction of motor impairment in comparison to usual care alone in...

Real-time interactive artificial intelligence of things-based prediction for adverse outcomes in adult patients with pneumonia in the emergency department.

BACKGROUND: Artificial intelligence of things (AIoT) may be a solution for predicting adverse outcomes in emergency department (ED) patients with pneu...

Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.

Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic ...

What's in a trauma? Using machine learning to unpack what makes an event traumatic.

What differentiates a trauma from an event that is merely upsetting? Wildly different definitions of trauma have been used in both formal (psychiatric...

Bone collision detection method for robot assisted fracture reduction based on force curve slope.

BACKGROUND AND OBJECTIVE: The application of robot technology in fracture reduction ensures the minimal invasiveness and accurate operation process. M...

Platform for Healthcare Promotion and Cardiovascular Disease Prevention.

This article presents the hardware-software design and implementation of an open, integrated, and scalable healthcare platform oriented to multiple po...

Deep Learning in the Detection of Rare Fractures - Development of a "Deep Learning Convolutional Network" Model for Detecting Acetabular Fractures.

BACKGROUND: Fracture detection by artificial intelligence and especially Deep Convolutional Neural Networks (DCNN) is a topic of growing interest in c...

Emerging Technologies for In Vitro Inhalation Toxicology.

Respiratory toxicology remains a major research area in the 21st century since current scenario of airborne viral infection transmission and pollutant...

Atraumatic Insertion of a Cochlear Implant Pre-Curved Electrode Array by a Robot-Automated Alignment with the Coiling Direction of the Scala Tympani.

INTRODUCTION: Electrode array translocation is an unpredictable event with all types of arrays, even using a teleoperated robot in a clinical scenario...