Convolutional neural networks for wound detection: the role of artificial intelligence in wound care.

Journal: Journal of wound care
Published Date: Oct 1, 2019

Abstract

OBJECTIVE: Telemedicine is an essential support system for clinical settings outside the hospital. Recently, the importance of the model for assessment of telemedicine (MAST) has been emphasised. The development of an eHealth-supported wound assessment system using artificial intelligence is awaited. This study explored whether or not wound segmentation of a diabetic foot ulcer (DFU) and a venous leg ulcer (VLU) by a convolutional neural network (CNN) was possible after being educated using sacral pressure ulcer (PU) data sets, and which CNN architecture was superior at segmentation.

Authors

  • Norihiko Ohura
    1 Department of Plastic, Reconstructive Surgery, Kyorin University School of Medicine, Tokyo, Japan.
  • Ryota Mitsuno
    2 Computer Biomedical Imaging, KYSMO.inc, Nagoya, Japan.
  • Masanobu Sakisaka
    1 Department of Plastic, Reconstructive Surgery, Kyorin University School of Medicine, Tokyo, Japan.
  • Yuta Terabe
    1 Department of Plastic, Reconstructive Surgery, Kyorin University School of Medicine, Tokyo, Japan.
  • Yuki Morishige
    1 Department of Plastic, Reconstructive Surgery, Kyorin University School of Medicine, Tokyo, Japan.
  • Atsushi Uchiyama
    2 Computer Biomedical Imaging, KYSMO.inc, Nagoya, Japan.
  • Takumi Okoshi
    2 Computer Biomedical Imaging, KYSMO.inc, Nagoya, Japan.
  • Iizaka Shinji
    3 School of Nutrition, College of Nursing and Nutrition, Shukutoku University, Chiba, Japan.
  • Akihiko Takushima
    1 Department of Plastic, Reconstructive Surgery, Kyorin University School of Medicine, Tokyo, Japan.

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