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Burns

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Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning.

Biomedical physics & engineering express
Evaluation of skin recovery is an important step in the treatment of burns. However, conventional methods only observe the surface of the skin and cannot quantify the injury volume. Optical coherence tomography (OCT) is a non-invasive, non-contact, r...

Adversarial attacks and adversarial training for burn image segmentation based on deep learning.

Medical & biological engineering & computing
Deep learning has been widely applied in the fields of image classification and segmentation, while adversarial attacks can impact the model's results in image segmentation and classification. Especially in medical images, due to constraints from fac...

[Research progress on the application of artificial intelligence in the early diagnosis and treatment of burn diseases].

Zhonghua wei zhong bing ji jiu yi xue
Artificial intelligence (AI) technology is advancing rapidly, constantly presenting its application value and broad prospects in the medical field. Especially in the early intervention of burn diseases, the new developments, applications, and challen...

Motion-Mimicking Robotic Finger Prosthesis for Burn-induced Partial Hand Amputee: A Case Report.

Journal of burn care & research : official publication of the American Burn Association
Burn injuries often result in severe hand complications, including joint contractures and nerve damage, sometimes leading to amputation. Despite early treatment, hypertrophic scarring frequently hampers hand function recovery, and the thick raised sc...

Comparing Artificial Intelligence Guided Image Assessment to Current Methods of Burn Assessment.

Journal of burn care & research : official publication of the American Burn Association
Appropriate identification of burn depth and size is paramount. Despite the development of burn depth assessment aids [eg, laser Doppler imaging (LDI)], clinical assessment, which assesses partial-thickness burn depth with 67% accuracy, currently rem...

Machine Learning-Based Prediction of Delirium and Risk Factor Identification in Intensive Care Unit Patients With Burns: Retrospective Observational Study.

JMIR formative research
BACKGROUND: The incidence of delirium in patients with burns receiving treatment in the intensive care unit (ICU) is high, reaching up to 77%, and has been associated with increased mortality rates. Therefore, early identification of patients at high...