Thermal injury to skin and subcutaneous tissue is common in both civilian and combat scenarios. Understanding the change in tissue morphologies and properties and the underlying mechanisms of thermal injury are of vital importance to clinical determination of the degree of burn and treatment approach. This review aims at summarizing the research involving experimental and numerical studies of skin and subcutaneous tissue subjected to thermal injury. The review consists of two parts. The first part deals with experimental studies including burn protocols and prevailing imaging approaches. The second part deals with existing numerical models for burn injuries of tissue and related computational simulations. Based on this review, we conclude that though there is literature contributing to the knowledge of the pathology and pathogenesis of tissue burn, there is scant quantitative information regarding changes in tissue properties including mechanical, thermal, electrical and optical properties as a result of burn injuries that are linked to altered tissue morphology.

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This study utilizes Raman spectroscopy to analyze the burn-induced collagen conformational changes in ex vivo porcine skin tissue. Raman spectra of wavenumbers 500–2000 cm−1 were measured for unburnt skin as well as four different burn conditions: (i) 200 °F for 10 s, (ii) 200 °F for the 30 s, (iii) 450 °F for 10 s and (iv) 450 °F for 30 s. The overall spectra reveal that protein and amino acids-related bands have manifested structural changes including the destruction of protein-related functional groups, and transformation from α-helical to disordered structures which are correlated with increasing burn severity. The deconvolution of the amide I region (1580–1720 cm−1) and the analysis of the sub-bands reveal a change of the secondary structure of the collagen from the α-like helix dominated to the β-aggregate dominated one. Such conformational changes may explain the softening of mechanical response in burnt tissues reported in the literature.

This article presents a real-time approach for classification of burn depth based on B-mode ultrasound imaging. A grey-level co-occurrence matrix (GLCM) computed from the ultrasound images of the tissue is employed to construct the textural feature set and the classification is performed using nonlinear support vector machine and kernel Fisher discriminant analysis. A leave-one-out cross-validation is used for the independent assessment of the classifiers. The model is tested for pair-wise binary classification of four burn conditions in ex vivo porcine skin tissue: (i) 200 °F for 10 s, (ii) 200 °F for 30 s, (iii) 450 °F for 10 s, and (iv) 450 °F for 30 s. The average classification accuracy for pairwise separation is 99% with just over 30 samples in each burn group and the average multiclass classification accuracy is 93%. The results highlight that the ultrasound imaging-based burn classification approach in conjunction with the GLCM texture features provide an accurate assessment of altered tissue characteristics with relatively moderate sample sizes, which is often the case with experimental and clinical datasets. The proposed method is shown to have the potential to assist with the real-time clinical assessment of burn degrees, particularly for discriminating between superficial and deep second degree burns, which is challenging in clinical practice.

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