Interpretable deep learning method to predict wound healing progress based on collagen fibers in wound tissue.

Journal: Computers in biology and medicine
PMID: 40198981

Abstract

BACKGROUND AND OBJECTIVE: The dynamic evolution of collagen fibers during wound healing is crucial for assessing repair progression, guiding clinical treatment, and drug screening. Current quantitative methods analyzing collagen spatial patterns (density, orientation variance) lack established criteria to both stratify distinct healing periods and detect delayed healing conditions, necessitating the establishment of a novel classification method for wound healing status based on collagen fibers.

Authors

  • Juan He
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Xiaoyan Wang
    Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.
  • Zhengshan Wang
    Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, 999078, Macau.
  • Ruitao Xie
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Zhiming Zhang
    Shenzhen Prevention and Treatment Center for Occupational Diseases, Shenzhen 518020, China.
  • Tzu-Ming Liu
    Institute of Translational Medicine, Faculty of Health Sciences & Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau, Taipa Macau China.
  • Yunpeng Cai
    Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, People's Republic of China. yp.cai@siat.ac.cn.
  • Long Chen
    Department of Critical Care Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.

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