Characterizing the Suture Pullout Force for Human Small Bowel.

Performing a small bowel anastomosis, or reconnecting small bowel segments, remains a core competency and critical step for the successful surgical management of numerous bowel and urinary conditions. As surgical education and technology moves toward improving patient outcomes through automation and increasing training opportunities, a detailed characterization of the interventional biomechanical properties of the human bowel is important. This is especially true due to the prevalence of anastomotic leakage as a frequent (3.02%) postoperative complication of small bowel anastomoses. This study aims to characterize the forces required for a suture to tear through human small bowel (suture pullout force, SPOF), while analyzing how these forces are affected by tissue orientation, suture material, suture size, and donor demographics. 803 tests were performed on 35 human small bowel specimens. A uni-axial test frame was used to tension sutures looped through 10 × 20 mm rectangular bowel samples to tissue failure. The mean SPOF of the small bowel was 4.62±1.40 N. We found no significant effect of tissue orientation (p = 0.083), suture material (p = 0.681), suture size (p = 0.131), age (p = 0.158), sex (p = .083), or body mass index (BMI) (p = 0.100) on SPOF. To our knowledge, this is the first study reporting human small bowel SPOF. Little research has been published about procedure-specific data on human small bowel. Filling this gap in research will inform the design of more accurate human bowel synthetic models and provide an accurate baseline for training and clinical applications.

Mechanical behavior of full-thickness burn human skin is rate-independent.

Skin tissue is recognized to exhibit rate-dependent mechanical behavior under various loading conditions. Here, we report that the full-thickness burn human skin exhibits rate-independent behavior under uniaxial tensile loading conditions. Mechanical properties, namely, ultimate tensile stress, ultimate tensile strain, and toughness, and parameters of Veronda-Westmann hyperelastic material law were assessed via uniaxial tensile tests. Univariate hypothesis testing yielded no significant difference (p > 0.01) in the distributions of these properties for skin samples loaded at three different rates of 0.3 mm/s, 2 mm/s, and 8 mm/s. Multivariate multiclass classification, employing a logistic regression model, failed to effectively discriminate samples loaded at the aforementioned rates, with a classification accuracy of only 40%. The median values for ultimate tensile stress, ultimate tensile strain, and toughness are computed as 1.73 MPa, 1.69, and 1.38 MPa, respectively. The findings of this study hold considerable significance for the refinement of burn care training protocols and treatment planning, shedding new light on the unique, rate-independent behavior of burn skin.

Cognitive Task Analysis of Escharotomy.

INTRODUCTION: With the Army's emerging doctrine of prolonged field care, and with burns being a common injury among soldiers, non-expert providers must be trained to perform escharotomy when indicated. However, the existing physical simulators and training protocols are not sufficient for training non-experts for performing effective escharotomy. Hence, to provide guidance in developing realistic escharotomy simulators and effective training protocols suitable for prolonged field care, a cognitive task analysis (CTA) is needed. This work aims to obtain educative information from expert burn surgeons regarding escharotomy procedures via the CTA. MATERIALS AND METHODS: The CTA was done by interviewing five subject matter experts with experience in performing escharotomy ranging from 20 to over 100 procedures and analyzing their responses. Interview questions were developed to obtain educative information from expert burn surgeons regarding the escharotomy procedure. A "gold standard protocol" was developed based on the CTA of each of the subject matter experts. RESULTS: The CTA helped identify general themes, including objectives, conditions that mandate escharotomy, signs of successful escharotomy, precautions, challenges, decisions, and performance standards, and specific learning goals such as the use of equipment, vital signs, performing the procedure, and preoperative and postoperative care. A unique aspect of this CTA is that it identifies the background information and preparations that could be useful to the practitioners at various levels of expertise. CONCLUSIONS: The CTA enabled us to compile a "gold standard protocol" for escharotomy that may serve as a guide for practitioners at various levels of expertise. This information will provide a framework for escharotomy training systems and simulators.

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.

A deep learning model for burn depth classification using ultrasound imaging.

Identification of burn depth with sufficient accuracy is a challenging problem. This paper presents a deep convolutional neural network to classify burn depth based on altered tissue morphology of burned skin manifested as texture patterns in the ultrasound images. The network first learns a low-dimensional manifold of the unburned skin images using an encoder-decoder architecture that reconstructs it from ultrasound images of burned skin. The encoder is then re-trained to classify burn depths. The encoder-decoder network is trained using a dataset comprised of B-mode ultrasound images of unburned and burned ex vivo porcine skin samples. The classifier is developed using B-mode images of burned in situ skin samples obtained from freshly euthanized postmortem pigs. The performance metrics obtained from 20-fold cross-validation show that the model can identify deep-partial thickness burns, which is the most difficult to diagnose clinically, with 99% accuracy, 98% sensitivity, and 100% specificity. The diagnostic accuracy of the classifier is further illustrated by the high area under the curve values of 0.99 and 0.95, respectively, for the receiver operating characteristic and precision-recall curves. A post hoc explanation indicates that the classifier activates the discriminative textural features in the B-mode images for burn classification. The proposed model has the potential for clinical utility in assisting the clinical assessment of burn depths using a widely available clinical imaging device.

Thermally damaged porcine skin is not a surrogate mechanical model of human skin.

Porcine skin is considered a de facto surrogate for human skin. However, this study shows that the mechanical characteristics of full thickness burned human skin are different from those of porcine skin. The study relies on five mechanical properties obtained from uniaxial tensile tests at loading rates relevant to surgery: two parameters of the Veronda-Westmann hyperelastic material model, ultimate tensile stress, ultimate tensile strain, and toughness of the skin samples. Univariate statistical analyses show that human and porcine skin properties are dissimilar (p < 0.01) for each loading rate. Multivariate classification involving the five mechanical properties using logistic regression can successfully separate the two skin types with a classification accuracy exceeding 95% for each loading rate individually as well as combined. The findings of this study are expected to guide the development of effective training protocols and high-fidelity simulators to train burn care providers.

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.