Utilizing Image Processing Techniques for Wound Management and Evaluation in Clinical Practice: Establishing the Feasibility of Implementing Artificial Intelligence in Routine Wound Care.

Objective: To develop a generalizable and accurate method for automatically analyzing wound images captured in clinical practice and extracting key wound characteristics such as surface area measurement.

Methods: The authors used image processing techniques to create a robust algorithm for segmenting pressure injuries from digital images captured by nurses during clinical practice. The algorithm also measured the real-world wound surface area. They used the hue-saturation-value color space to analyze red color values and to detect and segment the wound region within the entire image. To assess the accuracy of the algorithm's wound segmentation, the authors compared the results against wound image annotations.

Results: The algorithm performed impressively, achieving an intersection-over-union score of up to 0.85 and 100% intersection with the annotations. The algorithm effectively analyzed wound images obtained during clinical practice and accurately extracted the surface area of the documented pressure injuries. These results support the feasibility and applicability of this methodology.

Conclusions: Accurate determination of wound size and healing supports decision-making regarding treatment and is essential to successful outcomes. This innovative approach for visual assessment of chronic wounds highlights the potential of computerized wound analysis in clinical practice. By leveraging advanced computational techniques, healthcare providers can gain valuable insights into wound progression, enabling more accurate assessments to support their decision-making.