Exploring Combat Tourniquet Application Errors and Duration Among Skilled and Novice Medical Trainees.

INTRODUCTION: The use of tourniquets in combat medicine continues to be a key focus as they have consistently been shown to combat one of the leading causes of preventable death on the battlefield, massive hemorrhage to extremities. The present study analyzed tourniquet application among combat medics (68W) and combat lifesavers (CLSs) in a training environment to determine whether trainees' performance is consistent among one another and whether performance can be associated with participant demographics such as experience or role. MATERIALS AND METHODS: Study participants treated male and female patient simulators within a tactical field care phase, both of which experienced an amputated leg and required the application of a Combat Application Tourniquet (CAT). To assess tourniquet application variability and performance, a series of application subtasks and potential errors were measured via video coding of the scenarios by a team of 5 coders. Time to tourniquet application and tourniquet application duration were also coded to assess correlations between application duration and variability or performance. RESULTS: Results from analyzing tourniquet application subtasks and errors through a series of one-way ANOVA tests showed that application of the CAT first, hasty CAT application, and high tourniquet application were not predictive of participant role, time within the role, and self-reported tourniquet skill, confidence, or experience. Such demographic variables were also not predictive of successful tourniquet application as defined by the number of windlass rod rotations. Results from binomial logistic regressions showed that participant role and self-reported tourniquet skill and experience were predictors of tourniquet application duration. CONCLUSION: The findings suggest that high variability in CAT application methodology and performance exists among CLS and combat medics, which is largely not predictable by various demographics such as role, experience within the designated role, and self-reported confidence, skill, or experience. The observed disconnect between training or experience and CAT application performance suggests substantial variability in the consistency of training for both CLS and 68W soldiers. These inconsistencies may stem from variability in instructor knowledge, teaching styles, or training materials or may be developed through informal methods such as experiences in the field or recommendations from colleagues and experts. These findings highlight a potential need to reassess CAT application training, particularly in regard to consistency and validation. Finally, it should be noted that the study's findings may be limited or fail to capture some study effects because of the sample size and wide range of reported experience among participants.

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.

One-shot skill assessment in high-stakes domains with limited data via meta learning.

Deep Learning (DL) has achieved robust competency assessment in various high-stakes fields. However, the applicability of DL models is often hampered by their substantial data requirements and confinement to specific training domains. This prevents them from transitioning to new tasks where data is scarce. Therefore, domain adaptation emerges as a critical element for the practical implementation of DL in real-world scenarios. Herein, we introduce A-VBANet, a novel meta-learning model capable of delivering domain-agnostic skill assessment via one-shot learning. Our methodology has been tested by assessing surgical skills on five laparoscopic and robotic simulators and real-life laparoscopic cholecystectomy. Our model successfully adapted with accuracies up to 99.5 % in one-shot and 99.9 % in few-shot settings for simulated tasks and 89.7 % for laparoscopic cholecystectomy. This study marks the first instance of a domain-agnostic methodology for skill assessment in critical fields setting a precedent for the broad application of DL across diverse real-life domains with limited data.

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.

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.

Assessment of Surgical Tasks Using Neuroimaging Dataset (ASTaUND).

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool for studying brain activity in mobile subjects. Open-access fNIRS datasets are limited to simple and/or motion-restricted tasks. Here, we report a fNIRS dataset acquired on mobile subjects performing Fundamentals of Laparoscopic Surgery (FLS) tasks in a laboratory environment. Demonstrating competency in the FLS tasks is a prerequisite for board certification in general surgery in the United States. The ASTaUND data set was acquired over four different studies. We provide the relevant information about the hardware, FLS task execution protocols, and subject demographics to facilitate the use of this open-access data set. We also provide the concurrent FLS scores, a quantitative metric for surgical skill assessment developed by the FLS committee. This data set is expected to support the growing field of assessing surgical skills via neuroimaging data and provide an example of data processing pipeline for use in realistic, non-restrictive environments.

Variability of tissue mechanical response in Sus Domesticus porcine models from in vivo to ex vivo conditions.

BACKGROUND: Healthcare simulators have been demonstrated to be a valuable resource for training several technical and nontechnical skills. A gap in the fidelity of tissues has been acknowledged as a barrier to application for current simulators; especially for interventional procedures. Inaccurate or unrealistic mechanical response of a simulated tissue to a given surgical tool motion may result in negative training transfer and/or prevents the "suspension of disbelief" necessary for a trainee to engage in the activity. Thus, where it is relevant to training outcomes, there should be an effort to create healthcare simulators with simulated tissue mechanical responses that match or represent those of biological tissues. Historically, this data is most often gathered from preserved (post mortem) tissue; however, there is a concern that the mechanical properties of preserved tissue, that lacks blood flow, may lack adequate accuracy to provide the necessary training efficacy of simulators. METHODS AND FINDINGS: This work explores the effect of the "state" of the tissue testing status on liver and peritoneal tissue by using a customized handheld grasper to measure the mechanical responses of representative porcine (Sus domesticus) tissues in n = 5 animals across five test conditions: in vivo, post mortem (in-situ), ex vivo (immediately removed from fresh porcine cadaver), post-refrigeration, and post-freeze-thaw cycle spanning up to 72 hours after death. No statistically significant difference was observed in the mechanical responses due to grasping between in vivo and post-freeze conditions for porcine liver and peritoneum tissue samples (p = 0.05 for derived stiffness at grasping force values F = 5N and 6.5N). Furthermore, variance between in vivo and post-freeze conditions within each animal, was comparable to the variance of the in vivo condition between animals. CONCLUSIONS: Results of this study further validate the use of preserved tissue in the design of medical simulators via observing tissue mechanical responses of post-freeze tissue comparable to in vivo tissue. Therefore, the use of thawed preserved tissue for the further study and emulation of mechanical perturbation of the liver and peritoneum can be considered. Further work in this area should investigate these trends further, particularly in regard to other tissues and the potential effects varying preservation methods may yield.

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.

Automated Video Debriefing Using Computer Vision Techniques.

INTRODUCTION: Within any training event, debriefing is a vital component that highlights areas of proficiency and deficiency, enables reflection, and ultimately provides opportunity for remediation. Video-based debriefing is desirable to capture performance and replay events, but the reality is rife with challenges, principally lengthy video and occlusions that block line of sight from camera equipment to participants. METHODS: To address this issue, researchers automated the editing of a video debrief, using a system of person-worn cameras and computer vision techniques. The cameras record a simulation event, and the video is processed using computer vision. Researchers investigated a variety of computer vision techniques, ultimately focusing on the scale invariant feature transform detection method and a convolutional neural network. The system was trained to detect and tag medically relevant segments of video and assess a single exemplar medical intervention, in this case the application of a tourniquet. RESULTS: The system tagged medically relevant video segments with 92% recall and 66% precision, resulting in an F1 (harmonic mean of precision and recall) of 72% (N = 23). The exemplar medical intervention was successfully assessed in 39.5% of videos (N = 39). CONCLUSION: The system showed suitable accuracy tagging medically relevant video segments, but requires additional research to improve medical intervention assessment accuracy. Computer vision has the potential to automate video debrief creation to augment existing debriefing strategies.

Brain-behavior analysis of transcranial direct current stimulation effects on a complex surgical motor task.

Transcranial Direct Current Stimulation (tDCS) has demonstrated its potential in enhancing surgical training and performance compared to sham tDCS. However, optimizing its efficacy requires the selection of appropriate brain targets informed by neuroimaging and mechanistic understanding. Previous studies have established the feasibility of using portable brain imaging, combining functional near-infrared spectroscopy (fNIRS) with tDCS during Fundamentals of Laparoscopic Surgery (FLS) tasks. This allows concurrent monitoring of cortical activations. Building on these foundations, our study aimed to explore the multi-modal imaging of the brain response using fNIRS and electroencephalogram (EEG) to tDCS targeting the right cerebellar (CER) and left ventrolateral prefrontal cortex (PFC) during a challenging FLS suturing with intracorporeal knot tying task. Involving twelve novices with a medical/premedical background (age: 22-28 years, two males, 10 females with one female with left-hand dominance), our investigation sought mechanistic insights into tDCS effects on brain areas related to error-based learning, a fundamental skill acquisition mechanism. The results revealed that right CER tDCS applied to the posterior lobe elicited a statistically significant (q < 0.05) brain response in bilateral prefrontal areas at the onset of the FLS task, surpassing the response seen with sham tDCS. Additionally, right CER tDCS led to a significant (p < 0.05) improvement in FLS scores compared to sham tDCS. Conversely, the left PFC tDCS did not yield a statistically significant brain response or improvement in FLS performance. In conclusion, right CER tDCS demonstrated the activation of bilateral prefrontal brain areas, providing valuable mechanistic insights into the effects of CER tDCS on FLS peformance. These insights motivate future investigations into the effects of CER tDCS on error-related perception-action coupling through directed functional connectivity studies.

Error-related brain state analysis using electroencephalography in conjunction with functional near-infrared spectroscopy during a complex surgical motor task.

Error-based learning is one of the basic skill acquisition mechanisms that can be modeled as a perception-action system and investigated based on brain-behavior analysis during skill training. Here, the error-related chain of mental processes is postulated to depend on the skill level leading to a difference in the contextual switching of the brain states on error commission. Therefore, the objective of this paper was to compare error-related brain states, measured with multi-modal portable brain imaging, between experts and novices during the Fundamentals of Laparoscopic Surgery (FLS) "suturing and intracorporeal knot-tying" task (FLS complex task)-the most difficult among the five psychomotor FLS tasks. The multi-modal portable brain imaging combined functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for brain-behavior analysis in thirteen right-handed novice medical students and nine expert surgeons. The brain state changes were defined by quasi-stable EEG scalp topography (called microstates) changes using 32-channel EEG data acquired at 250 Hz. Six microstate prototypes were identified from the combined EEG data from experts and novices during the FLS complex task that explained 77.14% of the global variance. Analysis of variance (ANOVA) found that the proportion of the total time spent in different microstates during the 10-s error epoch was significantly affected by the skill level (p < 0.01), the microstate type (p < 0.01), and the interaction between the skill level and the microstate type (p < 0.01). Brain activation based on the slower oxyhemoglobin (HbO) changes corresponding to the EEG band power (1-40 Hz) changes were found using the regularized temporally embedded Canonical Correlation Analysis of the simultaneously acquired fNIRS-EEG signals. The HbO signal from the overlying the left inferior frontal gyrus-opercular part, left superior frontal gyrus-medial orbital, left postcentral gyrus, left superior temporal gyrus, right superior frontal gyrus-medial orbital cortical areas showed significant (p < 0.05) difference between experts and novices in the 10-s error epoch. We conclude that the difference in the error-related chain of mental processes was the activation of cognitive top-down attention-related brain areas, including left dorsolateral prefrontal/frontal eye field and left frontopolar brain regions, along with a 'focusing' effect of global suppression of hemodynamic activation in the experts, while the novices had a widespread stimulus(error)-driven hemodynamic activation without the 'focusing' effect.

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.

Error related fNIRS-EEG microstate analysis during a complex surgical motor task.

Fundamentals of Laparoscopic Surgery (FLS) is a standard education and training module with a set of basic surgical skills. During surgical skill acquisition, novices need to learn from errors due to perturbations in their performance which is one of the basic principles of motor skill acquisition. This study on thirteen healthy novice medical students and nine expert surgeons aimed to capture the brain state during error epochs using multimodal brain imaging by combining functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG). We performed error-related microstate analysis in the latent space that was found using regularized temporally embedded Canonical Correlation Analysis from fNIRS-EEG recordings during the performance of FLS "suturing and intracorporeal knot-tying" task - the most difficult among the five psychomotor FLS tasks. We found from two-way analysis of variance (ANDVA) with factors, skill level (expert, novice), and microstate type (1-6) that the proportion of the total time spent in microstates in the error epochs was significantly affected by the skill level ( ), microstate type ( ), and the interaction between the skill level and the microstate type ( ). Therefore, our study highlighted the relevance of portable brain imaging to capture error behavior when comparing the skill level during a complex surgical task. Clinical Relevance-This establishes the brain-behavior relationship for monitoring complex surgical motor task errors that differentiated experts from novices.

Directed information flow during laparoscopic surgical skill acquisition dissociated skill level and medical simulation technology.

Virtual reality (VR) simulator has emerged as a laparoscopic surgical skill training tool that needs validation using brain-behavior analysis. Therefore, brain network and skilled behavior relationship were evaluated using functional near-infrared spectroscopy (fNIRS) from seven experienced right-handed surgeons and six right-handed medical students during the performance of Fundamentals of Laparoscopic Surgery (FLS) pattern of cutting tasks in a physical and a VR simulator. Multiple regression and path analysis (MRPA) found that the FLS performance score was statistically significantly related to the interregional directed functional connectivity from the right prefrontal cortex to the supplementary motor area with F (2, 114) = 9, p < 0.001, and R2 = 0.136. Additionally, a two-way multivariate analysis of variance (MANOVA) found a statistically significant effect of the simulator technology on the interregional directed functional connectivity from the right prefrontal cortex to the left primary motor cortex (F (1, 15) = 6.002, p = 0.027; partial η2 = 0.286) that can be related to differential right-lateralized executive control of attention. Then, MRPA found that the coefficient of variation (CoV) of the FLS performance score was statistically significantly associated with the CoV of the interregionally directed functional connectivity from the right primary motor cortex to the left primary motor cortex and the left primary motor cortex to the left prefrontal cortex with F (2, 22) = 3.912, p = 0.035, and R2 = 0.262. This highlighted the importance of the efference copy information from the motor cortices to the prefrontal cortex for postulated left-lateralized perceptual decision-making to reduce behavioral variability.

Deep learning-based motion artifact removal in functional near-infrared spectroscopy.

Significance: Functional near-infrared spectroscopy (fNIRS), a well-established neuroimaging technique, enables monitoring cortical activation while subjects are unconstrained. However, motion artifact is a common type of noise that can hamper the interpretation of fNIRS data. Current methods that have been proposed to mitigate motion artifacts in fNIRS data are still dependent on expert-based knowledge and the post hoc tuning of parameters. Aim: Here, we report a deep learning method that aims at motion artifact removal from fNIRS data while being assumption free. To the best of our knowledge, this is the first investigation to report on the use of a denoising autoencoder (DAE) architecture for motion artifact removal. Approach: To facilitate the training of this deep learning architecture, we (i) designed a specific loss function and (ii) generated data to mimic the properties of recorded fNIRS sequences. Results: The DAE model outperformed conventional methods in lowering residual motion artifacts, decreasing mean squared error, and increasing computational efficiency. Conclusion: Overall, this work demonstrates the potential of deep learning models for accurate and fast motion artifact removal in fNIRS data.

Understanding the Cognitive Demands, Skills, and Assessment Approaches for Endotracheal Intubation: Cognitive Task Analysis.

BACKGROUND: Proper airway management is an essential skill for hospital personnel and rescue services to learn, as it is a priority for the care of patients who are critically ill. It is essential that providers be properly trained and competent in performing endotracheal intubation (ETI), a widely used technique for airway management. Several metrics have been created to measure competence in the ETI procedure. However, there is still a need to improve ETI training and evaluation, including a focus on collaborative research across medical specialties, to establish greater competence-based training and assessments. Training and evaluating ETI should also incorporate modern, evidence-based procedural training methodologies. OBJECTIVE: This study aims to use the cognitive task analysis (CTA) framework to identify the cognitive demands and skills needed to proficiently perform a task, elucidate differences between novice and expert performance, and provide an understanding of the workload associated with a task. The CTA framework was applied to ETI to capture a broad view of task and training requirements from the perspective of multiple medical specialties. METHODS: A CTA interview was developed based on previous research into the tasks and evaluation methods of ETI. A total of 6 experts from across multiple medical specialties were interviewed to capture the cognitive skills required to complete this task. Interviews were coded for main themes, subthemes in each category, and differences among specialties. These findings were compiled into a skills tree to identify the training needs and cognitive requirements of each task. RESULTS: The CTA revealed that consistency in equipment setup and planning, through talk or think-aloud methods, is critical to successfully mastering ETI. These factors allow the providers to avoid errors due to patient characteristics and environmental factors. Variation among specialties derived primarily from the environment in which ETI is performed, subsequent treatment plans, and available resources. Anesthesiology typically represented the most ideal cases with a large potential for training, whereas paramedics faced the greatest number of constraints based on the environment and available equipment. CONCLUSIONS: Although the skills tree cannot perfectly capture the complexity and detail of all potential cases, it provided insight into the nuanced skills and training techniques used to prepare novices for the variability they may find in practice. Importantly, the CTA identified ways in which challenges faced by novices may be overcome and how this training can be applied to future cases. By making these implicit skills and points of variation explicit, they can be better translated into teachable details. These findings are consistent with previous studies looking at developing improved assessment metrics for ETI and expanding upon their work by delving into methods of feedback and strategies to assist novices.

Characterization of Puncture Forces of the Human Trachea and Cricothyroid Membrane.

Accurate human tissue biomechanical data represents a critical knowledge gap that will help facilitate the advancement of new medical devices, patient-specific predictive models, and training simulators. Tissues related to the human airway are a top priority, as airway medical procedures are common and critical. Placement of a surgical airway, though less common, is often done in an emergent (cricothyrotomy) or urgent (tracheotomy) fashion. This study is the first to report relevant puncture force data for the human cricothyroid membrane and tracheal annular ligaments. Puncture forces of the cricothyroid membrane and tracheal annular ligaments were collected from 39 and 42 excised human donor tracheas, respectively, with a mechanized load frame holding various surgical tools. The average puncture force of the cricothyroid membrane using an 11 blade scalpel was 1.01 ± 0.36 N, and the average puncture force of the tracheal annular ligaments using a 16 gauge needle was 0.98 ± 0.34 N. This data can be used to inform medical device and airway training simulator development as puncture data of these anatomies has not been previously reported.

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.

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.

Brain network effects related to physical and virtual surgical training revealed by Granger causality.

this study investigates the difference in effective connectivity among novice medical students trained on physical and virtual simulators to perform the Fundamental laparoscopic surgery (FLS) pattern cutting task (PC). We propose using dynamic spectral Granger causality (GC) in the frequency band of [0.01-0.07]Hz to measure the effect of surgical training on effective brain connectivity. To obtain the dynamics relationship between the cortical regions, we propose to use the short-time Fourier transform (STFT) method. FLS pattern cutting is a complex bimanual task requiring fine motor skills and increased brain activity. With this in mind, we have used high resolution functional near-infrared spectroscopy to leverage its high temporal resolution for capturing the change in hemodynamics (HbO2) in 14 healthy subjects. Analysis of variance (ANOVA) found a statistically significant difference in "LPMC granger causes RPMC" (LPMC→ RPMC) in the subject trained on these two simulator in the first 40 sec of the task. We showed that the directed brain connectivity was affected by the type of surgical simulator used for training the medical students.