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In the context of healthcare for chronic wound patients, especially those living in remote areas, alternative solutions for care at home are needed to avoid transportation and care for patients in hospitals. Based on the requirements of the clinical team, this paper proposes a solution that employs image processing to assist the caring personnel in objectively evaluating the wound as well as bio-printing of necessary gel-based patches for treating it. For reliable and secure connectivity, the solution leverages nomadic micro-networks based on technologies like 5G and beyond. The paper also dives into the key design aspects, considering aspects such as access control and interoperability. Regarding the image processing service, an evaluation of three promising wound surface detection algorithms using publicly available datasets is provided. The paper also includes lessons learned from the do-it-yourself bio-printer.

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Implementation Design and Validation Results of a Chronic Wound Management System Powered by Beyond 5G Private and Nomadic Networking

  • 会议论文

  • 发表日期：

    2025年

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  • Andreea Ancuta Corici,
  • Martin Hocquel-Hans,
  • Kalpana Chaudhary,
  • Simone Kuntz,
  • Marius Corici,
  • Benny Häusler,
  • Hemant Zope,
  • Anne Deter,
  • Anne Grohnert,
  • Nils Lahmann

摘要

In the context of healthcare for chronic wound patients, especially those living in remote areas, alternative solutions for care at home are needed to avoid transportation and care for patients in hospitals. Based on the requirements of the clinical team, this paper proposes a solution that employs image processing to assist the caring personnel in objectively evaluating the wound as well as bio-printing of necessary gel-based patches for treating it. For reliable and secure connectivity, the solution leverages nomadic micro-networks based on technologies like 5G and beyond. The paper also dives into the key design aspects, considering aspects such as access control and interoperability. Regarding the image processing service, an evaluation of three promising wound surface detection algorithms using publicly available datasets is provided. The paper also includes lessons learned from the do-it-yourself bio-printer.