ArticleWound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society
Detection of Hypergranulation Tissue in Chronic Wound Images Using Artificial Intelligence Algorithms.
Article in Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society. 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
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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.
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Who cites it
1 citing paper in PubMed.
- Detection of Hypergranulation Tissue in Chronic Wound Images Using Artificial Intelligence Algorithms.Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair SocietyArticle
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypergranulation in chronic wounds reflects impaired healing, leading to delayed recovery, increased risk of infection and higher treatment costs for healthcare systems. Despite its impact, hypergranulation is often misidentified in the early stages, hindering timely intervention. This study presents a deep learning-based method to distinguish between hypergranulated and non-hypergranulated tissue. The dataset comprised 6235 wound images from Hospital de la Santa Creu de Vic (Catalonia, Spain) and DFUC 2022, with balanced classes. Five architectures were evaluated using transfer learning: ViT, VGG16, RepVGG, MobileViT and RepGhost. RepGhost achieved the best results (81.4% accuracy, 89.4% area under the receiver operating characteristic curve [AUC]), outperforming both CNNs and transformers. Lightweight models (RepGhost, MobileViT) also demonstrated high performance, making them suitable for implementation on mobile devices. This tool may assist clinicians in the early detection of hypergranulation and improve wound treatment outcomes. This appears to be the first deep learning study on this condition to utilise a large, balanced dataset.
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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.