Image Based Artificial Intelligence in Wound Assessment: A Systematic Review

TL;DR

This systematic review analyzes recent advancements in AI-based image analysis for wound assessment, covering methods, systems, and applications to improve clinical wound care.

Contribution

It provides a comprehensive overview of AI-driven wound measurement, diagnosis, and assessment systems, highlighting recent research and technological developments.

Findings

Extensive review of over 250 articles on AI in wound care

Detailed analysis of wound segmentation and classification methods

Survey of hardware, software, and mobile applications for wound assessment

Abstract

Efficient and effective assessment of acute and chronic wounds can help wound care teams in clinical practice to greatly improve wound diagnosis, optimize treatment plans, ease the workload and achieve health related quality of life to the patient population. While artificial intelligence (AI) has found wide applications in health-related sciences and technology, AI-based systems remain to be developed clinically and computationally for high-quality wound care. To this end, we have carried out a systematic review of intelligent image-based data analysis and system developments for wound assessment. Specifically, we provide an extensive review of research methods on wound measurement (segmentation) and wound diagnosis (classification). We also reviewed recent work on wound assessment systems (including hardware, software, and mobile apps). More than 250 articles were retrieved from various publication databases and online resources, and 115 of them were carefully selected to cover the breadth and depth of most recent and relevant work to convey the current review to its fulfillment.