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Published on: February 17, 2021
Utilizing Image Processing Techniques for Wound Management and Evaluation in Clinical Practice: Establishing the
Mai Dabas1, Suzanne Kapp, Amit Gefen
1Mai Dabas is Master's Degree Student, Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel. Suzanne Kapp, PhD, RN, is Clinical Associate Professor, School of Health Sciences, Faculty of Medicine, Dentistry and Health Sciences, Department of Nursing, The University of Melbourne, Melbourne, Australia; and National Manager Wound Prevention and Management, Regis Aged Care, Camberwell, Victoria, Australia. Amit Gefen, PhD, is Professor of Biomedical Engineering and the Herbert J. Berman Chair in Vascular Bioengineering, Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel; Skin Integrity Research Group (SKINT), University Centre for Nursing and Midwifery, Department of Public Health and Primary Care, Ghent University, Ghent, Belgium; and Department of Mathematics and Statistics and the Data Science Institute, Faculty of Sciences, Hasselt University, Hasselt, Belgium. Acknowledgments: This work was supported by a competitive grant from the Victorian Medical Research Acceleration Fund, with funding co-contribution from the Department of Nursing at the University of Melbourne, the Melbourne Academic Centre for Health, and Mölnlycke Health Care. This work was also partially supported by the Israeli Ministry of Science & Technology (Medical Devices Program grant no. 3-17421, awarded to Professor Amit Gefen in 2020). The authors thank Ms Carla Bondini for her assistance with data collection and management for this study and Mr Daniel Kapp for proofreading the manuscript. The authors have disclosed no other financial relationships related to this article. Submitted February 1, 2024; accepted in revised form April 16, 2024.
A new algorithm accurately measures wound surface area from clinical images, aiding treatment decisions. This automated wound analysis enhances chronic wound care and patient outcomes.
Area of Science:
- Medical image analysis
- Computational pathology
- Digital health
Background:
- Accurate wound assessment is crucial for effective treatment and healing.
- Current methods for wound measurement can be subjective and time-consuming.
- Automated analysis of wound images offers potential for improved objectivity and efficiency.
Purpose of the Study:
- To develop a generalizable and accurate automated method for analyzing wound images.
- To extract key wound characteristics, including surface area, from clinical photographs.
- To validate the algorithm's performance against manual annotations.
Main Methods:
- Utilized image processing techniques and the hue-saturation-value color space for wound segmentation.
- Developed a robust algorithm to segment pressure injuries from digital images captured in clinical practice.
- Measured real-world wound surface area using the developed algorithm.
Main Results:
- The algorithm achieved an intersection-over-union score of up to 0.85.
- Demonstrated 100% intersection with manual wound image annotations.
- Successfully and accurately extracted surface area measurements for pressure injuries from clinical images.
Conclusions:
- Computerized wound analysis shows significant potential for clinical practice.
- This innovative approach supports enhanced decision-making for chronic wound management.
- Advanced computational techniques can provide valuable insights into wound progression and healing.
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