Non-invasive Imaging Techniques for Predicting Healing Status of Diabetic Foot Ulcers: A Ten-year Systematic Review

Nila Sari^1,2Quoc Cuong Ngo¹Nemuel Daniel Pah^1,3*Rajna Ogrin⁴Elif Ilhan Ekinci^5,6Akram Al-Hourani¹Barbara Polus¹Dinesh Kumar¹

• ¹RMIT University, Melbourne, Australia
• ²Politeknik Negeri Bandung, Bandung, Indonesia
• ³Universitas Surabaya, Surabaya, Indonesia
• ⁴Bolton Clarke Research Institute, Melbourne, VIC, Australia
• ⁵The University of Melbourne, Melbourne, Australia
• ⁶Austin Health, Heidelberg, Australia

Early and accurate detection of diabetes-related foot ulcers (DFU) that may become chronic is essential to prevent long-term disability, amputation, and mortality. Various non-invasive imaging techniques have been developed to detect and monitor DFU progression, but none have yet been widely adopted in clinical practice. This review summarizes current advancements in non-invasive image techniques for DFU wound healing prediction and identifies research directions to support clinical translation. A systematic, multi-disciplinary review was conducted focusing on three imaging methods: photographic, hyperspectral, and thermal imaging. Articles published between July 2014 and July 2024 were searched across five databases: PubMed, Scopus, CINAHL, Embase, and Web of Science. The search was limited to English-language, peer-reviewed journal articles. The review followed PRISMA guidelines and applied the CASP quality appraisal tool. The initial search identified 2,937 articles, of which 22 studies met the inclusion criteria, including 17 original studies (9 medical and 8 engineering) on DFU healing prediction using imaging techniques and 5 relevant review articles. Each imaging method offers specific benefits and faces unique limitations: photographic imaging is user-friendly but lighting-sensitive; thermal imaging reflects inflammation but requires multimodal integration; hyperspectral imaging provides biochemical insight but is costly and less portable.Visual and thermal imaging, in particular, demonstrate strong potential for early and real-time prediction when combined with machine learning/deep learning. These methods offer portability, ease of use, and potential for automated analysis on a single device, making them suitable for clinical and community settings.However, challenges such as standardization and integration complexity remain. Continued research with larger datasets and improved validation is needed to enhance clinical readiness.