A statistical machine learning approach linking molecular conformational changes to altered mechanical characteristics of skin due to thermal injury.

This article develops statistical machine learning models to predict the mechanical properties of skin tissue subjected to thermal injury based on the Raman spectra associated with conformational changes of the molecules in the burned tissue. Ex vivo porcine skin tissue samples were exposed to controlled burn conditions at 200 °F for five different durations: (i) 10s, (ii) 20s, (iii) 30s, (iv) 40s, and (v) 50s. For each burn condition, Raman spectra of wavenumbers 500-2000 cm-1 were measured from the tissue samples, and tensile testing on the same samples yielded their material properties, including, ultimate tensile strain, ultimate tensile stress, and toughness. Partial least squares regression models were established such that the Raman spectra, describing conformational changes in the tissue, could accurately predict ultimate tensile stress, toughness, and ultimate tensile strain of the burned skin tissues with R2 values of 0.8, 0.8, and 0.7, respectively, using leave-two-out cross validation scheme. An independent assessment of the resultant models showed that amino acids, proteins & lipids, and amide III components of skin tissue significantly influence the prediction of the properties of the burned skin tissue. In contrast, amide I has a lesser but still noticeable effect. These results are consistent with similar observations found in the literature on the mechanical characterization of burned skin tissue.

Synthetic tissues lack the fidelity for the use in burn care simulators.

This work compares the mechanical response of synthetic tissues used in burn care simulators from ten different manufacturers with that of ex vivo full thickness burned porcine skin as a surrogate for human skin tissues. This is of high practical importance since incorrect mechanical properties of synthetic tissues may introduce a negative bias during training due to the inaccurate haptic feedback from burn care simulator. A negative training may result in inadequately performed procedures, such as in escharotomy, which may lead to muscle necrosis endangering life and limb. Accurate haptic feedback in physical simulators is necessary to improve the practical training of non-expert providers for pre-deployment/pre-hospital burn care. With the U.S. Army's emerging doctrine of prolonged field care, non-expert providers must be trained to perform even invasive burn care surgical procedures when indicated. The comparison reported in this article is based on the ultimate tensile stress, ultimate tensile strain, and toughness that are measured at strain rates relevant to skin surgery. A multivariate analysis using logistic regression reveals significant differences in the mechanical properties of the synthetic and the porcine skin tissues. The synthetic and porcine skin tissues show a similar rate dependent behavior. The findings of this study are expected to guide the development of high-fidelity burn care simulators for the pre-deployment/pre-hospital burn care provider education.

Raman spectroscopy accurately classifies burn severity in an ex vivo model.

Accurate classification of burn severities is of vital importance for proper burn treatments. A recent article reported that using the combination of Raman spectroscopy and optical coherence tomography (OCT) classifies different degrees of burns with an overall accuracy of 85% [1]. In this study, we demonstrate the feasibility of using Raman spectroscopy alone to classify burn severities on ex vivo porcine skin tissues. To create different levels of burns, four burn conditions were designed: (i) 200°F for 10s, (ii) 200°F for 30s, (iii) 450°F for 10s and (iv) 450°F for 30s. Raman spectra from 500-2000cm-1 were collected from samples of the four burn conditions as well as the unburnt condition. Classifications were performed using kernel support vector machine (KSVM) with features extracted from the spectra by principal component analysis (PCA), and partial least-square (PLS). Both techniques yielded an average accuracy of approximately 92%, which was independently evaluated by leave-one-out cross-validation (LOOCV). By comparison, PCA+KSVM provides higher accuracy in classifying severe burns, while PLS performs better in classifying mild burns. Variable importance in the projection (VIP) scores from the PLS models reveal that proteins and lipids, amide III, and amino acids are important indicators in separating unburnt or mild burns (200°F), while amide I has a more pronounced impact in separating severe burns (450°F).

Real-time Burn Classification using Ultrasound Imaging.

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.

Burn-related Collagen Conformational Changes in ex vivo Porcine Skin using Raman Spectroscopy.

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 ß-aggregate dominated one. Such conformational changes may explain the softening of mechanical response in burnt tissues reported in the literature.

Ultrasound elastography reliably identifies altered mechanical properties of burned soft tissues.

Although burn injury to the skin and subcutaneous tissues is common in both civilian and military scenarios, a significant knowledge gap exists in quantifying changes in tissue properties as a result of burns. In this study, we present a noninvasive technique based on ultrasound elastography which can reliably assess altered nonlinear mechanical properties of a burned tissue. In particular, ex vivo porcine skin tissues have been exposed to four different burn conditions: (i) 200°F for 10s, (ii) 200°F for 30s, (iii) 450°F for 10s, and (iv) 450°F for 30s. A custom-developed instrument including a robotically controlled ultrasound probe and force sensors has been used to compress the tissue samples to compute two parameters (C10 and C20) of a reduced second-order polynomial hyperelastic material model. The results indicate that while the linear model parameter (C10) does not show a statistically significant difference between the test conditions, the nonlinear model parameter (C20) reliably identifies three (ii-iv) of the four cases (p<0.05) when comparing burned with unburned tissues with a classification accuracy of 60-87%. Additionally, softening of the tissue is observed because of the change in structure of the collagen fibers. The ultrasound elastography-based technique has potential for application under in vivo conditions, which is left for future work.

Thermal injury of skin and subcutaneous tissues: A review of experimental approaches and numerical models.

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 burns 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 burns that are linked to altered tissue morphology.