Multimodal sensing and therapeutic systems for wound healing and management: A review

• Published in: Sensors and actuators reports Vol. 4; p. 100075
• Main Authors: Lu, Shao-Hao, Samandari, Mohamadmahdi, Li, Caihong, Li, Huijie, Song, Dongjin, Zhang, Yi, Tamayol, Ali, Wang, Xueju
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2022; Elsevier
• ISSN: 2666-0539; 2666-0539
• DOI: 10.1016/j.snr.2022.100075

•Realtime physiological sensing of wound properties, including temperature, moisture, pressure, and strain.•Chemical sensing of the wound environment like pH, uric acid, and cytokine.•Therapeutic systems for wound management, including active drug delivery systems based on external stimulations and non-drug stimulations.•Data-driven assessment and management of the wound healing process (i.e., machine learning and deep learning). Wounds especially chronic ones significantly affect the quality of patients’ life and present a severe financial burden for the healthcare industry. Timely and effective management of wounds, such as diagnosing wound parameters, treating various wound symptoms, and reducing infection at the wound noninvasively, is very important for accelerating wound healing and relieving patients’ pain. Recent years have seen significant efforts dedicated to developing technologies for monitoring various biomarkers vital to the wound healing process including temperature, pressure, pH, and the infection status to assist with the diagnosis and treatment of wounds, as well as advanced wound therapies such as on-demand and local drug delivery. This review paper introduces recent progress on multimodal sensing and therapeutic systems for wound healing. Specifically, we focus on physical sensing (temperature, moisture, pressure, and strain), chemical sensing (pH, uric acid, and cytokine), as well as therapeutic systems for wound management (active drug delivery systems based on external stimulations and non-drug stimulations). In addition, leveraging advanced analytic techniques, i.e., machine learning and deep learning, for data-driven assessment and management of the wound healing process has been discussed. [Display omitted]