Artificial Intelligence-Assisted Conductive Hydrogel Dressings for Refractory Wounds Monitoring.

Journal: Nano-micro letters
Published Date: Jul 3, 2025

Abstract

Refractory wounds cause significant harm to the health of patients and the most common treatments in clinical practice are surgical debridement and wound dressings. However, certain challenges, including surgical difficulty, lengthy recovery times, and a high recurrence rate persist. Conductive hydrogel dressings with combined monitoring and therapeutic properties have strong advantages in promoting wound healing due to the stimulation of endogenous current on wounds and are the focus of recent advancements. Therefore, this review introduces the mechanism of conductive hydrogel used for wound monitoring and healing, the materials selection of conductive hydrogel dressings used for wound monitoring, focuses on the conductive hydrogel sensor to monitor the output categories of wound status signals, proving invaluable for non-invasive, real-time evaluation of wound condition to encourage wound healing. Notably, the research of artificial intelligence (AI) model based on sensor derived data to predict the wound healing state, AI makes use of this abundant data set to forecast and optimize the trajectory of tissue regeneration and assess the stage of wound healing. Finally, refractory wounds including pressure ulcers, diabetes ulcers and articular wounds, and the corresponding wound monitoring and healing process are discussed in detail. This manuscript supports the growth of clinically linked disciplines and offers motivation to researchers working in the multidisciplinary field of conductive hydrogel dressings.

Authors

  • Yumo She
    Department of Gastroenterology, Endoscopic Center, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China.
  • He Liu
    Division of Endodontics, Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, BC, Canada.
  • Hailiang Yuan
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, People's Republic of China.
  • Yiqi Li
    Shenzhen Institute of Molecular Crop Design Shenzhen, China.
  • Xunjie Liu
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, People's Republic of China.
  • Ruonan Liu
  • Mengyao Wang
    Key Laboratory of Luminescence and Real-Time Analytical Chemistry (Ministry of Education), College of Pharmaceutical Sciences, Southwest University, Chongqing 400716, China.
  • Tingting Wang
    Department of Anesthesiology, Taizhou Hospital, Linhai, China.
  • Lina Wang
    Department of Biochemistry and Molecular Biology, Shandong University School of MedicineJinan, P. R. China; Central Laboratory, The Second Hospital of Shandong UniversityJinan, P. R. China.
  • Meihan Liu
    College of Computer Science, Zhejiang University, Hangzhou, 310027, China; Zhejiang Provincial Key Laboratory of Service Robot, Zhejiang University, Hangzhou, 310027, China. Electronic address: lmh_zju@zju.edu.cn.
  • Wenyu Wan
    Department of Dermatology, The First Hospital of China Medical University, Shenyang, 110001, People's Republic of China. wwan111@outlook.com.
  • Ye Tian
    State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics and Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, 361102, China.
  • Kai Zhang
    Anhui Province Key Laboratory of Respiratory Tumor and Infectious Disease, First Affiliated Hospital of Bengbu Medical University, Bengbu, China.

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