SynthesisFrontiers in medical technology2025
Non-invasive imaging techniques for predicting healing status of diabetic foot ulcers: a ten-year systematic review.
Synthesis in Frontiers in medical technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Clinical applications of machine learning for infection assessment in diabetic foot ulcers.Frontiers in physiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: 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. Methods: 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. Results: 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. Discussion: 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.
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Registered trials
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