Unveiling the role of artificial intelligence for wound assessment and wound healing prediction

Abstract

Wound healing is a very dynamic and complex process as it involves the patient, wound-level parameters, as well as biological, environmental, and socioeconomic factors. Its process includes hemostasis, inflammation, proliferation, and remodeling. Evaluation of wound components such as angiogenesis, inflammation, restoration of connective tissue matrix, wound contraction, remodeling, and re-epithelization would detail the healing process. Understanding key mechanisms in the healing process is critical to wound research. Elucidating its healing complexity would enable control and optimize the processes for achieving faster healing, preventing wound complications, and undesired outcomes such as infection, periwound dermatitis and edema, hematomas, dehiscence, maceration, or scarring. Wound assessment is an essential step for selecting an appropriate treatment and evaluating the wound healing process. The use of artificial intelligence (AI) as advanced computer-assisted methods is promising for gaining insights into wound assessment and healing. As AI-based approaches have been explored for various applications in wound care and research, this paper provides an overview of recent studies exploring the application of AI and its technical developments and suitability for accurate wound assessment and prediction of wound healing. Several studies have been done across the globe, especially in North America, Europe, Oceania, and Asia. The results of these studies have shown that AI-based approaches are promising for wound assessment and prediction of wound healing. However, there are still some limitations and challenges that need to be addressed. This paper also discusses the challenges and limitations of AI-based approaches for wound assessment and prediction of wound healing. The paper concludes with a discussion of future research directions and recommendations for the use of AI-based approaches for wound assessment and prediction of wound healing.