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Applying AIoT image recognition for prognosis of wound healing in long-term care residential facility

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Abstract

As one of the hottest topics in medical imaging, artificial intelligence (AI) plays a major role in large-scale medical image processing. In many studies, it has been applied for automatic recognition of complex patterns in image data and different stages of treatment. As the world’s population is aging, countries around the world face a rapid increase in the demand for elderly care. The use of AI in medical image recognition supports clinical staff in their image analysis and facilitates faster diagnosis when there is a shortage of caregivers. It is particularly useful for the treatment of chronic wounds. Given the complexity and variety of issues associated with acute and chronic wounds, they are typically documented in diagnostic and nursing records with text descriptions and photographs. However, wounds may remain inflamed for a long time owing to the nursing staff's poor understanding of wound care procedures, inadequate wound assessment skills, and insufficient knowledge of the use of dressings. Not only does this cause physical and psychological suffering to patients, but also results in high medical expenses. To this end, this study proposes an AIoT-based prognostic assessment system that supports the treatment of pressure injuries in residential facilities. The system uses an intelligent mobile IoT device to quickly measure wound size and changes in tissue ratios with a deep recognition module, which allows caregivers to quickly classify wound stages and estimate the healing condition based on the system’s results. Clinically, it can help physicians and nursing staff to automatically generate a pressure injury report. This study conforms to the government’s guidelines on wound assessment, which cover causes of wounds, treatment goals, and care procedures. The system is intended to support physicians in clinical decision-making and nursing staff in efficient reporting. In addition, the system can be used for tracking patients’ health to reduce the risk of wound deterioration and to provide more targeted medical care for the elderly community.

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Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is supported by the National Science and Technology Council of Taiwan under grants MOST 111–2622-E-227–001 and MOST 109–2221-E-227–002-MY3.

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Authors and Affiliations

  1. Department of Innovative Living Design, Overseas Chinese University, Taichung, Taiwan, R.O.C.

    Chien-Liang Chen

  2. Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan, R.O.C.

    Shih-Chun Chiang & Lun-Ping Hung

  3. Department of Electrical and Computer Engineering, Tamkang University, Taipei, Taiwan, R.O.C.

    Syu-Jhih Jhang

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  1. Chien-Liang Chen
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Correspondence to Lun-Ping Hung.

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Chen, CL., Chiang, SC., Hung, LP. et al. Applying AIoT image recognition for prognosis of wound healing in long-term care residential facility. Wireless Netw (2023). https://doi.org/10.1007/s11276-023-03452-z

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