Recent Advances in Artificial Intelligence for Wound Assessment

Dear Colleagues,

Chronic wounds—including diabetic foot ulcers, venous leg ulcers and pressure injuries—represent a major and growing challenge for healthcare systems worldwide. These conditions affect millions of patients and are associated with substantial morbidity, mortality and healthcare costs. Accurate wound assessment is essential for guiding treatment decisions and predicting healing outcomes. However, current clinical practice still relies heavily on visual inspection and manual measurements, which can be subjective, time-consuming and prone to inter-observer variability.

Recent advances in artificial intelligence—particularly deep learning and computer vision—have created unprecedented opportunities to transform wound assessment and management. By leveraging large collections of wound images and clinical data, modern AI algorithms can automatically extract clinically meaningful features from wound images, enabling objective evaluation of wound characteristics such as size, tissue composition, infection status and healing progression. Image-based AI systems have demonstrated promising potential to support clinicians in diagnosis, automate wound measurement and tissue classification and predict healing trajectories, ultimately improving clinical decision-making and patient outcomes.

This Special Issue on “Recent Advances in Artificial Intelligence for Wound Assessment” aims to bring together cutting-edge research at the intersection of artificial intelligence, medical image analysis and clinical wound care. We welcome original research articles, methodological developments and comprehensive reviews that advance the development, validation and clinical translation of AI-driven wound assessment technologies.

Topics of interest include, but are not limited to:

1. Novel deep learning architectures for wound detection, segmentation and classification
2. Multimodal wound analysis integrating imaging, clinical data and electronic health records
3. Automated tissue type identification and quantitative wound measurement
4. Deep learning methods for wound healing prediction and prognosis
5. Few-shot, transfer learning and self-supervised learning for medical imaging
6. Explainable and trustworthy AI for clinical wound assessment
7. Mobile, point-of-care and telemedicine applications for wound monitoring
8. Clinical validation, deployment and translational studies of AI-based wound analysis systems

Prof. Dr. Zeyun Yu
Dr. D. M. Anisuzzaman
Guest Editors

Guest Editor Assistant:
Name: Dr. Yash Patel
Affiliation: College of Business and Information Technology, Lawrence Technological University
Southfield, MI 48075, USA
Website: https://ltu.edu/faculty/patel-yash/
E-mail: ypatel3@ltu.edu
Interests: big data; machine learning; image analysis

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• artificial intelligence
• deep learning
• medical image analysis
• convolutional neural networks
• wound assessment
• wound segmentation
• wound classification
• wound healing prediction
• telemedicine
• digital health