Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning.

Evaluation of skin recovery is an important step in the treatment of burns. However, conventional methods only observe the surface of the skin and cannot quantify the injury volume. Optical coherence tomography (OCT) is a non-invasive, non-contact, real-time technique. Swept source OCT uses near infrared light and analyzes the intensity of light echo at different depths to generate images from optical interference signals. To quantify the dynamic recovery of skin burns over time, laser induced skin burns in mice were evaluated using deep learning of Swept source OCT images. A laser-induced mouse skin thermal injury model was established in thirty Kunming mice, and OCT images of normal and burned areas of mouse skin were acquired at day 0, day 1, day 3, day 7, and day 14 after laser irradiation. This resulted in 7000 normal and 1400 burn B-scan images which were divided into training, validation, and test sets at 8:1.5:0.5 ratio for the normal data and 8:1:1 for the burn data. Normal images were manually annotated, and the deep learning U-Net model (verified with PSPNe and HRNet models) was used to segment the skin into three layers: the dermal epidermal layer, subcutaneous fat layer, and muscle layer. For the burn images, the models were trained to segment just the damaged area. Three-dimensional reconstruction technology was then used to reconstruct the damaged tissue and calculate the damaged tissue volume. The average IoU value and f-score of the normal tissue layer U-Net segmentation model were 0.876 and 0.934 respectively. The IoU value of the burn area segmentation model reached 0.907 and f-score value reached 0.951. Compared with manual labeling, the U-Net model was faster with higher accuracy for skin stratification. OCT and U-Net segmentation can provide rapid and accurate analysis of tissue changes and clinical guidance in the treatment of burns.

Segmentation and classification of skin burn images with artificial intelligence: Development of a mobile application.

AIM: This study was conducted to determine the segmentation, classification, object detection, and accuracy of skin burn images using artificial intelligence and a mobile application. With this study, individuals were able to determine the degree of burns and see how to intervene through the mobile application. METHODS: This research was conducted between 26.10.2021-01.09.2023. In this study, the dataset was handled in two stages. In the first stage, the open-access dataset was taken from https://universe.roboflow.com/, and the burn images dataset was created. In the second stage, in order to determine the accuracy of the developed system and artificial intelligence model, the patients admitted to the hospital were identified with our own design Burn Wound Detection Android application. RESULTS: In our study, YOLO V7 architecture was used for segmentation, classification, and object detection. There are 21018 data in this study, and 80% of them are used as training data, and 20% of them are used as test data. The YOLO V7 model achieved a success rate of 75.12% on the test data. The Burn Wound Detection Android mobile application that we developed in the study was used to accurately detect images of individuals. CONCLUSION: In this study, skin burn images were segmented, classified, object detected, and a mobile application was developed using artificial intelligence. First aid is crucial in burn cases, and it is an important development for public health that people living in the periphery can quickly determine the degree of burn through the mobile application and provide first aid according to the instructions of the mobile application.

Review of machine learning for optical imaging of burn wound severity assessment.

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.

In vivo visualization of burn depth in skin tissue of rats using hemoglobin parameters estimated by diffuse reflectance spectral imaging.

Significance: Burn injuries represent a global public health problem that kills an estimated 180,000 people annually. Non-fatal burns result in prolonged hospitalization, disfigurement, and disability. The most common, convenient, and widely used method for assessing burn depth is physical or visual examination, but the accuracy of this method is reportedly poor (60% to 75%). Rapid, correct assessment of burn depth is very important for the optimal management and treatment of burn patients. New methods of burn depth assessment that are inexpensive, simple, rapid, non-contact, and non-invasive are therefore needed. Aim: The aim of this study was to propose an approach to visualize the spatial distribution of burn depth using hemoglobin parameters estimated from spectral diffuse reflectance imaging and to demonstrate the feasibility of the proposed approach for differentiating burn depth in a rat model of scald burn injury. Approach: The new approach to creating a spatial map of burn depth was based on canonical discriminant analysis (CDA) of total hemoglobin concentration, tissue oxygen saturation, and methemoglobin saturation as estimated from spectral diffuse reflectance images. Burns of three different degrees of severity were created in rat dorsal skin by 10-s exposure to water maintained at 70°C, 78°C, and 98°C, respectively. Spectral images for dorsal regions were acquired under anesthesia immediately after burn injury and at 24 h, 48 h, and 72 h after injury. Results: Most areas of images in the group with skin exposed to 70°C water and 98°C water were classified as 70°C burn and 98°C burn, respectively. In contrast, no significant difference between areas classified as 78°C burn and 98°C burn from 24 h to 72 h was evident in the group with skin exposed to 78°C water, suggesting that burn depth was heterogeneous. Conclusions: The proposed approach combining diffuse reflectance spectral imaging and CDA appears promising for differentiating 70°C burns from 78°C burns and 98°C burns, and 98°C burns from 70°C burns and 78°C burns at 24 to 72 h after burn injury in a rat model of scald burn injury.

Non-invasive medical imaging technology for the diagnosis of burn depth.

Currently, the clinical diagnosis of burn depth primarily relies on physicians' judgements based on patients' symptoms and physical signs, particularly the morphological characteristics of the wound. This method highly depends on individual doctors' clinical experience, proving challenging for less experienced or primary care physicians, with results often varying from one practitioner to another. Therefore, scholars have been exploring an objective and quantitative auxiliary examination technique to enhance the accuracy and consistency of burn depth diagnosis. Non-invasive medical imaging technology, with its significant advantages in examining tissue surface morphology, blood flow in deep and changes in structure and composition, has become a hot topic in burn diagnostic technology research in recent years. This paper reviews various non-invasive medical imaging technologies that have shown potential in burn depth diagnosis. These technologies are summarized and synthesized in terms of imaging principles, current research status, advantages and limitations, aiming to provide a reference for clinical application or research for burn specialists.

Perfusion Analysis Using High-Definition Indocyanine Green Angiography in Burn Comb Model.

Indocyanine green angiography (ICGA) has been widely employed for quantitative evaluation of the rat comb burn model, but the imaging equipment, imaging protocol, and fluorescence data interpretation of ICGA remain unsatisfactory. This study aims to provide better solutions for the application of ICGA in perfusion analysis. The rat comb burn model was established under a series of different comb contact durations, including 10, 20, 25, 30, 35, and 40 s. Indocyanine green angiography was used to analyze wound perfusion. In total, 16 rats were divided into ibuprofen and control groups for the burn model, and their perfusion was compared. A total of 16 identical models were divided into standard- and high-dose indocyanine green (ICG) groups, and ICGA was conducted to investigate the dynamic change in wound fluorescence. Escharectomy was performed under real-time fluorescence mapping and navigation. The results showed that a comb contact duration of 30 s was optimum for the burn model. Indocyanine green angiography could accurately evaluate the histologically determined depth of thermal injury and wound perfusion in the rat comb model. Digital subtraction of residual fluorescence was necessary for multiple comparisons of perfusion. Dynamic changes in fluorescence and necrotic tissues were observed more clearly by high-dose (0.5 mg/kg) ICG in angiography. In conclusion, perfusion analysis by ICGA can be used to assess the histologically determined depth of thermal injury and the impact of a specific treatment on wound perfusion. Indocyanine green angiography can help to identify necrotic tissue. The above findings and related imaging protocols lay the foundation for future research.

In vivo monitoring of hemoglobin derivatives in a rat thermal injury model using spectral diffuse reflectance imaging.

INTRODUCTION: To demonstrate the feasibility of our previously proposed Diffuse reflectance spectral imaging (DRSI) method for in vivo monitoring of oxygenated hemoglobin, deoxygenated hemoglobin, methemoglobin, tissue oxygen saturation, and methemoglobin saturation in a rat scald burn wound model and assess whether the method could be used for differentiating the burn depth groups in rats based on the hemoglobin parameters. METHODOLOGY: Superficial dermal burns (SDBs), deep dermal burns (DDBs), and deep burns (DBs) were induced in rat dorsal skin using a Walker-Mason method. An approach based on multiple regression analysis for spectral diffuse reflectance images aided by Monte Carlo simulations for light transport was used to quantify the hemoglobin parameters. Canonical discriminant analysis (CDA) was performed to discriminate SDB, DDB, and DB. RESULTS: CDA using the total hemoglobin concentration, tissue oxygen saturation, and methemoglobin saturation as the independent variables showed good performance for discriminating the SDB, DDB, and DB groups immediately after burn injury and the SDB group from the DDB and DB groups 24-72 h after burn injury. CONCLUSIONS: The DRSI method with multiple regression analysis for quantification of oxygenated hemoglobin, deoxygenated hemoglobin, and methemoglobin proved to be reliable for monitoring these hemoglobin derivatives in the rat experimental burn injury model. The parameters of tissue oxygen saturation, methemoglobin saturation, and total hemoglobin concentration are promising for the differentiating the degree of burn injury using CDA.

Feasibility of intra-operative image guidance in burn excision surgery with multispectral imaging and deep learning.

BACKGROUND: Exposing a healthy wound bed for skin grafting is an important step during burn surgery to ensure graft take and maintain good functional outcomes. Currently, the removal of non-viable tissue in the burn wound bed during excision is determined by expert clinician judgment. Using a porcine model of tangential burn excision, we investigated the effectiveness of an intraoperative multispectral imaging device combined with artificial intelligence to aid clinician judgment for the excision of non-viable tissue. METHODS: Multispectral imaging data was obtained from serial tangential excisions of thermal burn injuries and used to train a deep learning algorithm to identify the presence and location of non-viable tissue in the wound bed. Following algorithm development, we studied the ability of two surgeons to estimate wound bed viability, both unaided and aided by the imaging device. RESULTS: The deep learning algorithm was 87% accurate in identifying the viability of a burn wound bed. When paired with the surgeons, this device significantly improved their abilities to determine the viability of the wound bed by 25% (p = 0.03). Each time a surgeon changed their decision after seeing the AI model output, it was always a change from an incorrect decision to excise more tissue to a correct decision to stop excision. CONCLUSION: This study provides insight into the feasibility of image-guided burn excision, its effect on surgeon decision making, and suggests further investigation of a real-time imaging system for burn surgery could reduce over-excision of burn wounds.

Laser speckle contrast imaging, an alternative to laser doppler imaging in clinical practice of burn wound care derivation of a color code.

OBJECTIVE: To develop a color code and to investigate the validity of Laser Speckle Contrast Imaging (LSCI) for measuring burn wound healing potential (HP) in burn patients as compared to the reference standard Laser Doppler Imaging (LDI). METHOD: A prospective, observational, cohort study was conducted in adult patients with acute burn wounds. The relationship between mean flux measured with LDI and mean perfusion units (PU) measured with LSCI was expressed in a regression formula. Measurements were performed between 2 and 5 days after the burn wound. The creation of a LSCI color code was done by mapping the clinically validated color code of the LDI to the corresponding values on the LSCI scale. To assess validity of the LSCI, the ability of the LSCI to discriminate between HP < 14 and ≥ 14 days and HP < 21 and original ≥ 21 days according to the LDI reference standard was evaluated, with calculation of receiver operating characteristics (ROC) curves. RESULTS: A total of 50 patients were included with a median age of 40 years and total body surface area burned of 6%. LSCI values of 143 PU and 113 PU were derived as the cut-off values for the need of conservative treatment (HP < 14 and ≥ 14 days) resp. surgical closure (HP < 21 and ≥ 21 days). These LSCI cut off values showed a good discrimination between HP 14 days versus ≥ 14 days (Area Under Curve (AUC)= 0.89; sensitivity 85% and specificity = 82%) and a good discrimination between HP 21 days versus ≥ 21 days (AUC of 0.89, sensitivity 81% and specificity 88%). CONCLUSION: This is the first study in which a color code for the LSCI in adult clinical burn patients has been developed. Our study reconfirms the good performance of the LSCI for prediction of burn wound healing potential. This provides additional evidence for the potential value of the LSCI in specialized burn care.

Ultrasound-based evaluation of loss of lean mass in patients with burns: A prospective longitudinal study.

OBJECTIVES: To evaluate the loss of lean mass in patients with burns using ultrasonography of the quadriceps muscle of the thigh. METHODS: A prospective longitudinal study was conducted using ultrasound of the quadriceps muscle of the thigh to assess the change in thickness in millimeters on days 1, 3 and 7 after study enrollment in 45 patients with burns who were admitted to a burn center (BTC) of a university hospital between April 2020 and September 2021. Patients burns on the thighs, which made it difficult to undertake examinations, were excluded. Depending on where they were admitted, patients were divided into ward and intensive care unit (ICU) patients. ICU patients were considered to have more severe injuries. The general data collected included age, sex, weight, height, area of body surface burn, burn degree and etiology, and airway injury. The data collected for all patients during hospitalization at the BTC were as follows: existence of chronic illness, requirement for mechanical ventilation, Simplified Acute Physiology Score 3 (SAPS 3) and Sequential Organ Failure Assessment (SOFA) on the first day of hospitalization in an intensive care bed in the burn treatment unit (BTU), health-related infection, feeding route, length of hospital stay, and time spent in the BTU. RESULTS: Loss of muscle thickness was observed in all patients between days 1 and 7. The median thickness for all patients on day 1 was 24.50 mm (ITQ 21.22-30.85) and on day 7 it was 18.80 (ITQ 16.07-23.62), with P = 0.0001. The variation in thigh quadricep muscle thickness between day 1 and day 3, a median of - 2.80 mm (ITQ - 3.52-2.02) was obtained for patients on the ward and - 2.50 mm (ITQ - 3.92 to - 1.47) for ICU patients. Between day 3 and day 7, the variation was - 2.55 mm (ITQ - 4.55 to - 1.25) for ward patients and - 2.10 mm (ITQ - 3.12 to - 1.15) for ICU patients. The median thickness variation assessed between day 1 and day 7 was - 4.95 mm (ITQ - 8.25 to - 3.70) for patients on the ward and - 4.40 mm (ITQ - 7, 35 to - 2.90) for ICU patients. A correlation was observed between the variation in muscle thickness in the interval between day 1 to day 3 and age (P = 0.035). CONCLUSIONS: Muscle loss occurred early and rapidly within the first seven days of hospitalization, reflecting the impact of burn injury on nutritional risk. An association was observed between muscle thickness loss and age, but no association was observed with the extent of burn, length of hospital stay, occurrence of health-related infections or mortality. These findings suggest the importance of monitoring muscle loss in these patients in planning nutritional therapy, early mobilization, and prevention of complications.

Ultrasonography for Skin and Soft Tissue Infections, Noninfectious Cysts, Foreign Bodies, and Burns in the Critical Care Setting.

There are multiple opportunities for the use of ultrasonography in the diagnosis of skin and soft tissue differentials. Ultrasonography is inexpensive, easily reproducible, and able to provide real-time data in situations where condition changes and progression are common. Not only does bedside ultrasonography provide the clinician an in-depth look beyond epidermal structures into body cavities, it remains a safe, nonionizing radiating, effective, cost-efficient, reliable, and accessible tool for the emergency management of life- and limb-threatening integumentary infections. Unnecessary invasive procedures are minimized, providing improved patient outcomes. Integumentary abnormalities secondary to trauma, surgery, and hospitalization are common among critical care patients. This article provides a brief overview and evidence-based recommendations for the use of ultrasonography in the critical care setting for integumentary system conditions, including common skin and soft tissue differentials, foreign bodies, and burn depth assessment.

A Deep Learning Image-to-Image Translation Approach for a More Accessible Estimator of the Healing Time of Burns.

OBJECTIVE: An accurate and timely diagnosis of burn severity is critical to ensure a positive outcome. Laser Doppler imaging (LDI) has become a very useful tool for this task. It measures the perfusion of the burn and estimates its potential healing time. LDIs generate a 6-color palette image, with each color representing a healing time. This technique has very high costs associated. In resource-limited areas, such as low- and middle-income countries or remote locations like space, where access to specialized burn care is inadequate, more affordable and portable tools are required. This study proposes a novel image-to-image translation approach to estimate burn healing times, using a digital image to approximate the LDI. METHODS: This approach consists of a U-net architecture with a VGG-based encoder and applies the concept of ordinal classification. Paired digital and LDI images of burns were collected. The performance was evaluated with 10-fold cross-validation, mean absolute error (MAE), and color distribution differences between the ground truth and the estimated LDI. RESULTS: Results showed a satisfactory performance in terms of low MAE ( 0.2370 ±0.0086). However, the unbalanced distribution of colors in the data affects this performance. SIGNIFICANCE: This novel and unique approach serves as a basis for developing more accessible support tools in the burn care environment in resource-limited areas.

Comparative analysis of denoising techniques in burn depth discrimination from burn hyperspectral images.

This study analyzes and compares the performance of five denoising techniques (Lee filter, gamma filter, principal component analysis, maximum noise fraction, and wavelet transform) in order to identify the most appropriate one that lead to the most accurate classification of burned tissue in hyperspectral images. Fifteen hyperspectral images of burned patients were acquired and denoising techniques were applied to each image. Spectral angle mapper classifier was used for data classification and the confusion matrix was used for quantitative evaluation of the performances of the denoising methods. The results revealed that gamma filter performed better than other denoising techniques with values of overall accuracy and kappa coefficient of 91.18% and 0.8958 respectively. The lowest performance was detected for principal component analysis. In conclusion, the gamma filter could be considered an optimal choice for noise reduction in burn hyperspectral images and could be used for a more accurate diagnosis of burn depth.

Laser doppler imaging - the role of poor burn perfusion in predicting healing time and guiding operative management.

AIM: To identify if the proportion of poor blood flow (blue) within an LDI (Laser doppler Imaging) image of a burn independently correlates with healing time. METHODS: Patient age, gender, burn type, and burn surface area were collected from the IBID (International Burn Injury Database). All LDI images were copied from the MoorLDI2-BI- Laser Doppler (MLDI) Scanner, onto Adobe Photoshop® version 2020 for pixel counting analysis and calculation of % TBSA (Total Body Surface Area) blue. Multiple linear regression analysis determined whether a proportional relationship was present for each parameter (age, gender, % TBSA Blue and comorbidities) with healing time. RESULTS: 110 patients with 197 burns were scanned with MLDI. Median age was 5 years (IQR 1-6). Median burn surface area was 1.5% (IQR 1-2.4). 56.4% of patients were male and patients were scanned an average of 2.68 days (SD±1.37) following burn injury. Number of physical comorbidities and age were found to have a statistically significant relationship with healing time (p = 0.03, p = 0.002). Gender and %TBSA blue did not have a statistically significant relationship with healing time (p = 0.07 and p = 0.058 respectively). We found a statistically significant difference in the mean healing time between burns with and without blue (3.43 weeks vs. 2.80 weeks, p = 0.0001). % TBSA Blue was more than four times higher in the operated group (0.48% vs. 0.11%) and was shown to have a statistically significant relationship with decision to operate (p = 0.027). Positive predictive value for the presence of blue on operative rate was 71.6%. Age, gender and number of comorbidities did not have a statistically significant influence on operative rate (p = 0.07, p = 0.50 and p = 0.49). CONCLUSION: % TBSA blue was not found to be a reliable independent indicator of burn healing time, but the presence of blue within an LDI image, advanced patient age and increased number of comorbidities did have a statistically significant relationship with healing time. This suggests their standardised inclusion into management decisions regarding intermediate depth burns is warranted.

Reliability and feasibility of skeletal muscle ultrasound in the acute burn setting.

OBJECTIVES: Despite the impact of muscle wasting after burn, tools to quantify muscle wasting are lacking. This multi-centre study examined the utility of ultrasound to measure muscle mass in acute burn patients comparing different methodologies. METHODS: B-mode ultrasound was used by two raters to determine feasibility and inter-rater reliability in twenty burned adults following admission. Quadriceps muscle layer thickness (QMLT) and rectus femoris cross-sectional area (RF-CSA) were measured, comparing the use of i) a single versus average measurements, ii) a proximal versus distal location for QMLT, and iii) a maximum- versus no-compression technique for QMLT. RESULTS: Analysis of twenty burned adults (50 years [95%CI 42-57], 32%TBSA [95%CI 23-40]) yielded ICCs of> 0.97 for QMLT (for either location and compression technique) and> 0.95 for RF-CSA, using average measurements. Relative minimal detectable changes were smaller using no-compression than maximum-compression (6.5% vs. 15%). Using no-compression to measure QMLT was deemed feasible for both proximal and distal locations (94% and 96% of attempted measurements). In 9.5% of cases maximum-compression was not feasible. 95% of RF-CSA measurements were successfully completed. CONCLUSION: Ultrasound provides feasible and reliable values of quadriceps muscle architecture that can be adapted to clinical scenarios commonly encountered in acute burn settings.

U-Net based Mapping from Digital Images to Laser Doppler Imaging for Burn Assessment.

The incidence of burn injuries is higher in low-and middle-income countries, and particularly in remote areas where the access to specialized burn assessment, care and recovery is limited. Given the high costs associated with one of the most used techniques to evaluate the severity of a burn, namely laser Doppler imaging (LDI), an alternative approach could be beneficial for remote locations. This study proposes a novel approach to estimate the LDI from digital images of a burn. The approach is a pixel-wise regression model based on convolutional neural networks. To minimize the dependency on the conditions in which the images are taken, the effect of two image normalization techniques is also studied. Results indicate that the model performs satisfactorily on average, presenting low mean absolute and squared errors and high structural similarity index. While no significant differences are found when changing the normalization of the images, the performance is affected by their quality. This suggests that changes in the intensity of the images do not alter the relevant information about the wound, whereas changes in brightness, contrast and sharpness do.

Role of Platelet-Rich Plasma Gel in Promoting Wound Healing Based on Medical Images of Wounds.

A wound is the pathological change of soft tissue under normal skin caused by various factors, such as collision, contusion, hot crush, avulsion, corrosive chemicals, operations, excessive wound tension after operations, local pressure that cannot be relieved for a long time, liquid immersion, local infection, and rejection reactions caused by allogeneic substances. The skin itself or its underlying soft tissue loses its integrity and continuity, thus losing its normal physiological function. Medical image analysis is a medical term that refers to the interdisciplinary fields of integrated medical imaging, artificial intelligence, digital image processing and analysis, mathematical modeling, and numerical algorithms. According to the time of wound formation, they can be divided into acute and chronic wounds. The common acute wounds include lacerations caused by trauma, surgical incisions, burns, and donor sites formed after skin graft operations. This article mainly studies the role of platelet-rich plasma gel nanocomposites in promoting wound healing. It is proven that ptt-rich plasma gel can significantly promote tissue repair and regeneration and accelerate wound healing in patients with severe burns. The atomic number of the nanocomposite has a better treatment effect on the nanoparticle approach. In this paper, chitosan nanocomposite membrane, nanocomposite algorithm, and the calculation method of enthalpy of formation of high alloy nanomaterials were used to study the role of ptt-rich plasma gel combined chitosan nanocomposite membrane loaded bone marrow stromal cells in promoting wound healing, and its effects were applied to the repair of special site burns, special burns, and different age burns. Good wound repair benefits from the correct treatment of the wound, which directly affects the stability and development of the internal environment. The difference in healing time between the two groups was statistically significant, and the recovery time of the PRP group was 0.001 less than that of the control group. The results showed that the wound healing time of the PRP group was significantly shorter than that of the control group (P < 0.05); after treatment, the content of VEGF in the wound tissue of the two groups increased, especially in the PRP group; the effective rate of the PRP group was 75.0%, which was higher than 68.8% of the control group. It can play an important role in the regulation of expression and the pathophysiological process of wound healing.

A computational method for the investigation of burn scars topology based on 3D optical scan.

Burn scar treatment is a difficult subject to address since the improper therapy can have a significant impact on people's quality of life. The evaluation of medical therapy over time should be based on objective measurement of the severity of burn scars and their progression. Unfortunately, most clinical assessments of scars are still reliant on physicians' subjective exams of patients. A profitable method to overcome the limitations of subjective assessment could be to leverage 3D scanning technologies. These could be used to retrieve the surface topology of burns. Accordingly, the goal of this study is to provide an objective approach for analysing the surface topology of burn scars using 3D scanning and roughness-based evaluation. In particular, two types of ISO-compliant profile and surface filters (Gaussian and Wavelet) derived from the analysis of roughness in the mechanical sector are implemented to discriminate form from roughness of scars. Once retrieved, the roughness surface is processed to derive a set of statistical parameters describing the scar surface topology. Three case studies were used to derive these parameters (a synthetic surface, an ostrich-skin surface and a set of scars). After the surface's roughness was determined, a comparison between healthy and unhealthy skin could be established. The devised methods prove their effectiveness in correctly retrieving the main surface characteristics of a burn scar. Therefore, by using the proposed method it will be possible to evaluate the effectiveness of medical therapy by comparing the healthy and scarred skin of a single subject.

Evaluation of Bromelain-Based Enzymatic Debridement Combined with Laser Doppler Imaging and Healing of Burn Wounds.

BACKGROUND Accurate estimation of burn depth is crucial for correct treatment decision making. Bromelain-based enzymatic debridement (ED) may improve clinical assessment of burn depth. Laser Doppler imaging (LDI) provides a valuable indicator of burn depth by analyzing microcirculation within tissue beds. This study aimed to evaluate bromelain-based enzymatic debridement combined with laser Doppler imaging and healing of 42 wounds in 19 patients with mixed second- and third-degree thermal burns. MATERIAL AND METHODS We included 42 wounds in 19 patients with mixed deep dermal and full-thickness thermal burns. All patients were treated with eschar-specific removal agent for ED. The perfusion of each wound after ED was assessed using LDI. Healing time was estimated by 2 experienced burn surgeons and marked by the observation of epithelization. The usefulness of the LDI performed after ED in predicting healing time was estimated. The findings were analyzed to determine a cut-off value for LDI that indicates if a burn will heal spontaneously. RESULTS We observed that burn wounds with higher mean perfusion healed faster. The analysis showed a strong relationship between perfusion after ED and healing time (Spearman rank correlation coefficient=-0.803). A mean perfusion greater than 296.89 indicated that the wound could heal spontaneously and does not require skin grafting. CONCLUSIONS LDI examination of an already debrided wound allows for a reliable assessment of perfusion at an early stage of treatment. The use of a safe and effective debridement method in conjunction with a non-invasive diagnostic tool could improve burn management.