An ingenious deep learning approach for pressure injury depth evaluation with limited data.

Journal: Journal of tissue viability
Published Date: May 22, 2024

Abstract

BACKGROUND: The development of models using deep learning (DL) to assess pressure injuries from wound images has recently gained attention. Creating enough supervised data is important for improving performance but is time-consuming. Therefore, the development of models that can achieve high performance with limited supervised data is desirable.

Authors

  • Kento Ikuta
    Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan.
  • Kohei Fukuoka
    Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan.
  • Yuka Kimura
    Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan.
  • Makoto Nakagaki
    Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan.
  • Makoto Ohga
    Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan.
  • Yoshiko Suyama
    Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan.
  • Maki Morita
    Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan.
  • Ryunosuke Umeda
    Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan.
  • Mamoru Konishi
    Focus Systems Corporation, 2-7-8 Higashi Gotanda, Shinagawa-ku, Tokyo, 141-0022, Japan.
  • Hiroyuki Nishikawa
    Focus Systems Corporation, 2-7-8 Higashi Gotanda, Shinagawa-ku, Tokyo, 141-0022, Japan.
  • Shunjiro Yagi
    Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan. Electronic address: yagishun68@gmail.com.

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