在临床实践中利用图像处理技术进行伤口管理和评估:确定在常规伤口护理中实施人工智能的可行性
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.
一个新的算法从临床图像中准确地测量伤口表面积,帮助治疗决策. 这种自动化的伤口分析可以改善慢性伤口护理和患者的治疗结果.
科学领域:
- 医疗图像分析 医学图像分析
- 计算病理学计算病理学
- 数字健康数字健康
背景情况:
- 准确的伤口评估对于有效的治疗和愈合至关重要.
- 目前的伤口测量方法可能是主观的,耗时的.
- 伤口图像的自动化分析提供了提高客观性和效率的潜力.
研究的目的:
- 开发一种可通用和准确的自动化方法来分析伤口图像.
- 从临床照片中提取关键的伤口特征,包括表面积.
- 通过手动注释来验证算法的性能.
主要方法:
- 利用图像处理技术和色调和值颜色空间进行伤口细分.
- 开发了一个强大的算法,从临床实践中捕获的数字图像中对压力损伤进行细分.
- 使用开发的算法测量了现实世界的伤口表面积.
主要成果:
- 该算法实现了高达0.85.85的交叉与联合得分.
- 证明了100%的交叉与手动伤口图像注释.
- 从临床图像中成功并准确地提取了压力损伤的表面积测量.
结论:
- 计算机伤口分析显示了临床实践的巨大潜力.
- 这种创新方法支持慢性伤口管理的增强决策.
- 先进的计算技术可以为伤口的进展和愈合提供宝贵的见解.
相关概念视频
Issues And Trends In Healthcare Delivery System
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Current Trends in Nursing II


