SynthesisInternational wound journal2020
Wound assessment, imaging and monitoring systems in diabetic foot ulcers: A systematic review.
Synthesis in International wound journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses that pooled it.
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
26 citing papers in PubMed, 3 syntheses or guidelines pooled it, 58 citations in OpenAlex.
- Non-invasive imaging techniques for predicting healing status of diabetic foot ulcers: a ten-year systematic review.Frontiers in medical technology · 2025Pooled it
- Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Wound assessment, imaging and monitoring systems in diabetic foot ulcers: A systematic review.International wound journal · 2020Pooled it
- AI-Based Models for Diabetic Foot Ulcer Assessment: Scoping Review.JMIR diabetes · 2026Review
- Validation of a Trained AI Imaging Model for Detecting Diabetic Foot Deformities.International wound journal · 2026Article
- Proposed dense variational autoencoder model integrated with contrastive learning for foot ulcer classification.Scientific reports · 2026Article
- PICO-based assessment and categorization of evidence for digital health interventions: an inductive framework development.Frontiers in digital health · 2026Review
- Eff-ReLU-Net: a deep learning framework for multiclass wound classification.BMC medical imaging · 2025Article
- E-DFu-Net: An efficient deep convolutional neural network models for Diabetic Foot Ulcer classification.Biomolecules & biomedicine · 2025Article
- Article
- Screening and monitoring of diabetic polyneuropathy in clinical practice: present and future with connected devices.Frontiers in neurology · 2025Article
- Venous leg ulcer healing as a determinant of quality of life in patients treated with unna boot: A quasi-experimental study.PloS one · 2025Observational
- A Multi-Center Prospective Study on the Healing of Neuro-Ischemic Ulcers in Singapore: A Prospective Cohort Study.Health science reports · 2025Article
- Non-invasive techniques for wound assessment: A comprehensive review.International wound journal · 2024Review
- Smart diabetic foot ulcer scoring system.Scientific reports · 2024Article
- Development of an explainable artificial intelligence model for Asian vascular wound images.International wound journal · 2024Article
- A prospective study on the wound healing and quality of life outcomes of patients with venous leg ulcers in Singapore-Interim analysis at 6 month follow up.International wound journal · 2023Article
- Clinical validation of a smartphone application for automated wound measurement in patients with venous leg ulcers.International wound journal · 2023Article
- Diabetic wounds and artificial intelligence: A mini-review.World journal of clinical cases · 2023Review
- Effectiveness of Semi-Supervised Active Learning in Automated Wound Image Segmentation.International journal of molecular sciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Patients with diabetes mellitus have a lifetime risk of 15% to 25% of developing diabetic foot ulcers (DFUs). DFU is associated with significant morbidity and mortality. Wound imaging systems are useful adjuncts in monitoring of wound progress. Our study aims to review existing literature on the available wound assessment and monitoring systems for DFU. This is a systematic review of articles from PubMed and Embase (1974 - March 2020). All studies related to wound assessment or monitoring systems in DFUs were included. Articles on other types of wounds, review articles, and non-English texts were excluded. Outcomes include clinical use, wound measurement statistics, hospital system integration, and other advantages and challenges. The search identified 531 articles. Seventeen full-text studies were eligible for the final analysis. Five modalities were identified: (a) computer applications or hand-held devices (n = 5), (b) mobile applications (n = 2), (c) optical imaging (n = 2), (d) spectroscopy or hyperspectral imaging (n = 4), and (e) artificial intelligence (n = 4). Most studies (n = 16) reported on wound assessment or monitoring. Only one study reported on data capturing. Two studies on the use of computer applications reported low inter-observer variability in wound measurement (inter-rater reliability >0.99, and inter-observer variability 15.9% respectively). Hand-held commercial devices demonstrated high accuracy (relative error of 2.1%-6.8%). Use of spectroscopy or hyperspectral imaging in prediction of wound healing has a sensitivity and specificity of 80% to 90% and 74%to 86%, respectively. Majority of the commercially available wound assessment systems have not been reviewed in the literature on measurement accuracy. In conclusion, most imaging systems are superior to traditional wound assessment. Wound imaging systems should be used as adjuncts in DFU monitoring.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.