AI Medical Compendium Journal:
Burns : journal of the International Society for Burn Injuries

Showing 11 to 20 of 24 articles

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 Jul 15, 2021
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...
Machine Learning Burns Automation Body Surface Area Humans Artificial Intelligence
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Towards artificial intelligence for identifying cases of suspected maltreatment in paediatric burns.

Burns : journal of the International Society for Burn Injuries May 15, 2021
Artificial Intelligence Surveys and Questionnaires Child Burns Humans Child Abuse
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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 Feb 8, 2021
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...
Semantics Photography Humans Child Burns Neural Networks, Computer Artificial Intelligence
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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 Dec 15, 2020
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...
Receptors, Immunologic Anticoagulants Shock Humans Burns Prospective Studies Blood Coagulation Disorders Biomarkers
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Convolution neural network for effective burn region segmentation of color images.

Burns : journal of the International Society for Burn Injuries Sep 12, 2020
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...
Image Processing, Computer-Assisted Burns Humans Neural Networks, Computer Machine Learning
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BPBSAM: Body part-specific burn severity assessment model.

Burns : journal of the International Society for Burn Injuries May 4, 2020
BACKGROUND AND OBJECTIVE: Burns are a serious health problem leading to several thousand deaths annually, and despite the growth of science and technology, automated burns diagnosis still remains a major challenge. Researchers have been exploring vis...
Burns Automation Photography Trauma Severity Indices Humans Deep Learning Support Vector Machine Forearm Injuries Back Injuries Hand Injuries Facial Injuries
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Effects of interactive robot-enhanced hand rehabilitation in treatment of paediatric hand-burns: A randomized, controlled trial with 3-months follow-up.

Burns : journal of the International Society for Burn Injuries Feb 11, 2020
PURPOSE: To evaluate the effectiveness of the robotic-assisted exercise with virtual gaming on total active range of motion (ROM) of the digits, hand grip strength (HGS), and hand function in children with hand burns.
Exercise Therapy Range of Motion, Articular Video Games Hand Injuries Hand Strength Robotics Burns Humans Virtual Reality Child Female Male
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Artificial intelligence and machine learning for predicting acute kidney injury in severely burned patients: A proof of concept.

Burns : journal of the International Society for Burn Injuries Jun 21, 2019
BACKGROUND: Burn critical care represents a high impact population that may benefit from artificial intelligence and machine learning (ML). Acute kidney injury (AKI) recognition in burn patients could be enhanced by ML. The goal of this study was to ...
Urine Artificial Intelligence Proof of Concept Study Machine Learning Acute Kidney Injury Burns Adult Middle Aged Male Creatinine Peptide Fragments Female Natriuretic Peptide, Brain Young Adult Lipocalin-2 Humans
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Effect of nano-silver, nano-copper, deconex and benzalkonium chloride on biofilm formation and expression of transcription regulatory quorum sensing gene (rh1R) in drug-resistance Pseudomonas aeruginosa burn isolates.

Burns : journal of the International Society for Burn Injuries Dec 28, 2017
BACKGROUND: Biofilm forming drug-resistant Pseudomonas aeruginosa are responsible for major death in burn center of different hospitals across the globe.
Bacterial Proteins Humans Biofilms Polymerase Chain Reaction Pseudomonas aeruginosa Disinfectants Silver Benzalkonium Compounds Burns Drug Resistance, Bacterial Copper Anti-Infective Agents Microbial Sensitivity Tests RNA, Messenger Metal Nanoparticles Gene Expression Anti-Infective Agents, Local
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A novel mathematical model to predict prognosis of burnt patients based on logistic regression and support vector machine.

Burns : journal of the International Society for Burn Injuries Jan 13, 2016
OBJECTIVE: To develop a mathematical model of predicting mortality based on the admission characteristics of 6220 burn cases.
Child, Preschool Child ROC Curve Support Vector Machine Principal Component Analysis Logistic Models Models, Theoretical Adult Middle Aged Infant Adolescent Infant, Newborn Aged Young Adult Body Surface Area Smoke Inhalation Injury Burns Trauma Severity Indices Humans Shock Multivariate Analysis Prognosis
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