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Skin tear classification using machine learning from digital RGB image.

Takuro Nagata1, Shuhei S Noyori2, Hiroshi Noguchi3

  • 1School of Public Health, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.

Journal of Tissue Viability
|April 27, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning algorithms can classify skin tear categories from digital images, aiding nurses in wound care. This technology helps assess skin tear severity using the Skin Tear Audit Research (STAR) classification system.

Keywords:
Digital image analysisRandom forestSTAR Skin tear classification systemSupport vector machineWound assessment

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Area of Science:

  • Medical imaging
  • Wound care technology
  • Machine learning in healthcare

Background:

  • Skin tears are traumatic wounds requiring accurate severity evaluation for effective management.
  • The Skin Tear Audit Research (STAR) classification system is crucial for standardizing skin tear assessment.
  • Digital image analysis offers a potential tool to support clinical wound assessment.

Purpose of the Study:

  • To develop a machine learning algorithm for classifying skin tear categories using digital images.
  • To integrate shape features of skin tears into a machine learning model for improved classification.
  • To support nurses in the assessment and management of skin tears, regardless of specialization.

Main Methods:

  • Skin tear images were segmented, and features of each segment were extracted.
  • Support vector machine and random forest algorithms were employed for classification.
  • The performance was evaluated using 31 images with a leave-one-out cross-validation method.

Main Results:

  • Support vector machine achieved 74% accuracy for segment classification and 69% for STAR category classification.
  • Random forest demonstrated 71% accuracy for segment classification and 63% for STAR category classification.
  • Both algorithms showed capability in classifying wound segments and STAR categories.

Conclusions:

  • Machine learning algorithms show promise in classifying skin tear categories from digital images.
  • This technology can assist nurses in managing skin tears, enhancing wound care consistency.
  • The developed algorithm could be a valuable tool for non-specialist healthcare providers in wound management.