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Real-time burn depth assessment using artificial networks: a large-scale, multicentre study.

Yuan Wang1, Zuo Ke1, Zhiyou He2

  • 1College of Computer Science and Technology, National Defense University of Science and Technology, Changsha, Hunan, China.

Burns : Journal of the International Society for Burn Injuries
|August 23, 2020
PubMed
Summary
This summary is machine-generated.

This study developed an artificial intelligence model to accurately determine burn depth using wound images. The model aids in early diagnosis, improving treatment plans for burn patients.

Keywords:
Burn depth assessmentClassificationConvolutional neural networksMedical imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Burn Surgery

Background:

  • Accurate burn depth assessment is crucial for effective treatment planning.
  • Artificial Intelligence (AI) shows promise in enhancing early clinical diagnosis in medical imaging.
  • Limited progress in AI for burn depth recognition exists due to data limitations.

Purpose of the Study:

  • To develop a novel AI model for recognizing burn depth.
  • To improve the accuracy of early clinical diagnosis for burn injuries.
  • To assist in formulating accurate treatment plans for burn patients.

Main Methods:

  • Collected 484 early wound images from burn patients within 48 hours of discharge.
  • Manually annotated images by five burn surgeons, categorizing them into shallow, moderate, and deep burn depths based on healing time.
  • Utilized a pre-trained ResNet50 model with transfer learning, dividing 5637 image patches into training, validation, and test sets.

Main Results:

  • Established a novel convolutional neural network-based artificial burn depth recognition model.
  • Achieved a diagnostic accuracy of approximately 80% for distinguishing between shallow, moderate, and deep burns.
  • Demonstrated the model's ability to infer patient healing time and burn depth.

Conclusions:

  • Burn depth can be deduced from actual healing time.
  • The developed AI model accurately infers healing time and burn depth.
  • The model is expected to serve as an auxiliary diagnostic tool to improve clinical decision-making.