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Burns

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

Burns : journal of the International Society for Burn Injuries
BACKGROUND: Patients with severe burns often show systemic coagulation changes in the early stage and even develop extensive coagulopathy. Previous studies have confirmed that soluble TREM-like transcript-1 (sTLT-1) mediates a novel mechanism of haem...

Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept.

Scientific reports
Sepsis is the primary cause of burn-related mortality and morbidity. Traditional indicators of sepsis exhibit poor performance when used in this unique population due to their underlying hypermetabolic and inflammatory response following burn injury....

Convolution neural network for effective burn region segmentation of color images.

Burns : journal of the International Society for Burn Injuries
BACKGROUND: Burn injuries are one of the most severe forms of wounds and trauma across the globe. Automated burn diagnosis methods are needed to provide timely treatment to the concerned patients. Artificial intelligence is playing a vital role in de...

A Framework for Automatic Burn Image Segmentation and Burn Depth Diagnosis Using Deep Learning.

Computational and mathematical methods in medicine
Burn is a common traumatic disease with high morbidity and mortality. The treatment of burns requires accurate and reliable diagnosis of burn wounds and burn depth, which can save lives in some cases. However, due to the complexity of burn wounds, th...

Improving burn depth assessment for pediatric scalds by AI based on semantic segmentation of polarized light photography images.

Burns : journal of the International Society for Burn Injuries
This paper illustrates the efficacy of an artificial intelligence (AI) (a convolutional neural network, based on the U-Net), for the burn-depth assessment using semantic segmentation of polarized high-performance light camera images of burn wounds. T...

Enhancing Military Burn- and Trauma-Related Acute Kidney Injury Prediction Through an Automated Machine Learning Platform and Point-of-Care Testing.

Archives of pathology & laboratory medicine
CONTEXT.—: Delayed recognition of acute kidney injury (AKI) results in poor outcomes in military and civilian burn-trauma care. Poor predictive ability of urine output (UOP) and creatinine contribute to the delayed recognition of AKI.

A systematic review of machine learning and automation in burn wound evaluation: A promising but developing frontier.

Burns : journal of the International Society for Burn Injuries
BACKGROUND: Visual evaluation is the most common method of evaluating burn wounds. Its subjective nature can lead to inaccurate diagnoses and inappropriate burn center referrals. Machine learning may provide an objective solution. The objective of th...

Potential for Machine Learning in Burn Care.

Journal of burn care & research : official publication of the American Burn Association
Burn-related injuries are a leading cause of morbidity across the globe. Accurate assessment and treatment have been demonstrated to reduce the morbidity and mortality. This essay explores the forms of artificial intelligence to be implemented the fi...