Macrophages are innate immune cells that help wounds heal. Here, we study the potential immunomodulatory effects of negative-pressure wound therapy (NPWT) materials on the macrophage inflammatory response. We compared the effects of two materials, Granufoam™ (GF) and Veraflo Cleanse™ (VC), on macrophage function in vitro. We find that both materials cause reduced expression of inflammatory genes, such as TNF and IL1B, in human macrophages stimulated with bacterial lipopolysaccharide (LPS) and interferon-gamma (IFNγ). Relative to adherent glass control surfaces, VC discourages macrophage adhesion and spreading, and may potentially sequester LPS/IFNγ and cytokines that the cells produce. GF, on the other hand, was less suppressive of inflammation, supported macrophage adhesion and spreading better than VC, and sequestered lesser quantities of LPS/IFNγ in comparison to VC. The control dressing material cotton gauze (CT) was also immunosuppressive, capable of TNF-α retention and LPS/IFNγ sequestration. Our findings suggest that NPWT material interactions with cells, as well as soluble factors including cytokines and LPS, can modulate the immune response, independent of vacuum application. We have also established methodological strategies for studying NPWT materials and reveal the potential utility of cell-based in vitro studies for elucidating biological effects of NPWT materials.

Significance

Over the past decade, machine learning (ML) algorithms have rapidly become much more widespread for numerous biomedical applications, including the diagnosis and categorization of disease and injury.

Aim

Here, we seek to characterize the recent growth of ML techniques that use imaging data to classify burn wound severity and report on the accuracies of different approaches.

Approach

To this end, we present a comprehensive literature review of preclinical and clinical studies using ML techniques to classify the severity of burn wounds.

Results

The majority of these reports used digital color photographs as input data to the classification algorithms, but recently there has been an increasing prevalence of the use of ML approaches using input data from more advanced optical imaging modalities (e.g., multispectral and hyperspectral imaging, optical coherence tomography), in addition to multimodal techniques. The classification accuracy of the different methods is reported; it typically ranges from ∼70% to 90% relative to the current gold standard of clinical judgment.

Conclusions

The field would benefit from systematic analysis of the effects of different input data modalities, training/testing sets, and ML classifiers on the reported accuracy. Despite this current limitation, ML-based algorithms show significant promise for assisting in objectively classifying burn wound severity.