Skin tear classification using machine learning from digital RGB image.

Journal: Journal of tissue viability
Published Date: Jan 16, 2021

Abstract

AIM: Skin tears are traumatic wounds characterised by separation of the skin layers. Severity evaluation is important in the management of skin tears. To support the assessment and management of skin tears, this study aimed to develop an algorithm to estimate a category of the Skin Tear Audit Research classification system (STAR classification) using digital images via machine learning. This was achieved by introducing shape features representing complicated shape of the skin tears.

Authors

  • Takuro Nagata
    School of Public Health, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan. Electronic address: nagata-takuro-kanazawa@g.ecc.u-tokyo.ac.jp.
  • Shuhei S Noyori
    Department of Gerontological Nursing/Wound Care Management, The Division of Health Sciences and Nursing, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; Graduate Program for Social ICT Global Creative Leaders, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo, 102-0083, Japan. Electronic address: noyori-tky@umin.ac.jp.
  • Hiroshi Noguchi
    Department of Life Support Technology (Molten), Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan. Electronic address: hnogu-tky@umin.ac.jp.
  • Gojiro Nakagami
    Global Nursing Research Center, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Aya Kitamura
    Department of Gerontological Nursing/Wound Care Management, The Division of Health Sciences and Nursing, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan. Electronic address: ayakitamura@g.ecc.u-tokyo.ac.jp.
  • Hiromi Sanada
    Global Nursing Research Center, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

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