Application of multiple deep learning models for automatic burn wound assessment.

Journal: Burns : journal of the International Society for Burn Injuries
PMID: 35945064

Abstract

PURPOSE: Accurate assessment of the percentage of total body surface area (%TBSA) burned is crucial in managing burn injuries. It is difficult to estimate the size of an irregular shape by inspection. Many articles reported the discrepancy of estimating %TBSA burned by different doctors. We set up a system with multiple deep learning (DL) models for %TBSA estimation, as well as the segmentation of possibly poor-perfused deep burn regions from the entire wound.

Authors

  • Che Wei Chang
    Graduate Institute of Biomedical Electronics & Bioinformatics, National Taiwan University, Taipei, Taiwan.
  • Chun Yee Ho
    Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan.
  • Feipei Lai
    Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Room 410, Barry Lam Hall, No.1, Sec.4, Roosevelt Road, Taipei, 10617, Taiwan, Republic of China.
  • Mesakh Christian
    Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan.
  • Shih Chen Huang
    Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan.
  • Dun Hao Chang
    Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan.
  • Yo Shen Chen
    Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan.

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