Emergency Medicine

Deep Learning in Toxicologic Pathology: A New Approach to Evaluate Rodent Retinal Atrophy.

Quantification of retinal atrophy, caused by therapeutics and/or light, by manual measurement of retinal layers is labor intensive and time-consuming. In this study, we explored the role of deep learning (DL) in automating the assessment of retinal atrophy, particularly of the outer and inner nuclear layers, in rats. Herein, we report our experience creating and employing a hybrid approach, which ...

Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology.

Digital tissue image analysis is a computational method for analyzing whole-slide images and extracting large, complex, and quantitative data sets. However, as with any analysis method, the quality of generated results is dependent on a well-designed quality control system for the entire digital pathology workflow. Such system requires clear procedural controls, appropriate user training, and invo...

Development of Inappropriate Vasopressin Secretion in Association With Lumbar Cerebrospinal Fluid Drainage in an Adult With Traumatic Basilar Skull Fracture.

OBJECTIVE: Hyponatremia associated with cerebrospinal fluid (CSF) drainage is primarily limited to pediatric patients. Only 1 case in an adult, after ...

Improving ED Emergency Severity Index Acuity Assignment Using Machine Learning and Clinical Natural Language Processing.

INTRODUCTION: Triage is critical to mitigating the effect of increased volume by determining patient acuity, need for resources, and establishing acui...

Automated Cerebral Hemorrhage Detection Using RAPID.

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) is an important event that is diagnosed on head NCCT. Increased NCCT utilization in busy hospita...

Validation of the Al-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator in Patients 65 Years and Older.

OBJECTIVE: We sought to assess the performance of the Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) tool in elderly emergency surgery (E...

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models.

In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from studies toward studies. Currentl...

Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning.

BACKGROUND: Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating...

Combining Data with Predictions for Modeling Hepatic Steatosis by Using Stratified Bagging and Conformal Prediction.

Hepatic steatosis (fatty liver) is a severe liver disease induced by the excessive accumulation of fatty acids in hepatocytes. In this study, we devel...

Deep learning algorithms for brain disease detection with magnetic induction tomography.

PURPOSE: In order to improve the reconstruction accuracy of magnetic induction tomography (MIT) and achieve fast imaging especially in the detection o...

Machine Learning Revealed New Correlates of Chronic Pelvic Pain in Women.

Chronic pelvic pain affects one in seven women worldwide, and there is an urgent need to reduce its associated significant costs and to improve women'...

Improving rib fracture detection accuracy and reading efficiency with deep learning-based detection software: a clinical evaluation.

OBJECTIVES: To investigate the impact of deep learning (DL) on radiologists' detection accuracy and reading efficiency of rib fractures on CT.

Low-soluble TREM-like transcript-1 levels early after severe burn reflect increased coagulation disorders and predict 30-day mortality.

BACKGROUND: Patients with severe burns often show systemic coagulation changes in the early stage and even develop extensive coagulopathy. Previous st...

Development of a Self-Harm Monitoring System for Victoria.

The prevention of suicide and suicide-related behaviour are key policy priorities in Australia and internationally. The World Health Organization has ...

A deep learning approach for identifying cancer survivors living with post-traumatic stress disorder on Twitter.

BACKGROUND: Emotions after surviving cancer can be complicated. The survivors may have gained new strength to continue life, but some of them may begi...

An Intelligent Augmented Reality Training Framework for Neonatal Endotracheal Intubation.

Neonatal Endotracheal Intubation (ETI) is a critical resuscitation skill that requires tremendous practice of trainees before clinical exposure. Howev...

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

BACKGROUND: The early clinical course of COVID-19 can be difficult to distinguish from other illnesses driving presentation to hospital. However, vira...

Fully automated detection and segmentation of intracranial aneurysms in subarachnoid hemorrhage on CTA using deep learning.

In aneurysmal subarachnoid hemorrhage (aSAH), accurate diagnosis of aneurysm is essential for subsequent treatment to prevent rebleeding. However, ane...

Machine learning to predict hemorrhage and thrombosis during extracorporeal membrane oxygenation.

Moderate to severe leukocytosis with vasopressor use is associated with increased mortality in trauma patients.

BACKGROUND: Leukocytosis is a rise in white blood cell (WBC) count and clinical outcomes of moderate to severe leukocytosis in trauma patients have no...