Dual-imaging system for burn depth diagnosis

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.

Consensus on the treatment of second-degree burn wounds (2024 edition)

Second-degree burns are the most common type of burn in clinical practice and hard to manage. Their treatment requires not only a consideration of the different outcomes that may arise from the dressing changes or surgical therapies themselves but also an evaluation of factors such as the burn site, patient age and burn area. Meanwhile, special attention should be given to the fact that there is no unified standard or specification for the diagnosis, classification, surgical procedure, and infection diagnosis and grading of second-degree burn wounds. This not only poses great challenges to the formulation of clinical treatment plans but also significantly affects the consistency of clinical studies. Moreover, currently, there are relatively few guidelines or expert consensus for the management of second-degree burn wounds, and no comprehensive and systematic guidelines or specifications for the treatment of second-degree burns have been formed. Therefore, we developed the Consensus on the Treatment of Second-Degree Burn Wounds (2024 edition), based on evidence-based medicine and expert opinion. This consensus provides specific recommendations on prehospital first aid, nonsurgical treatment, surgical treatment and infection treatment for second-degree burns. The current consensus generated a total of 58 recommendations, aiming to form a standardized clinical treatment plan.

Implementing AI Models for Prognostic Predictions in High-Risk Burn Patients

BACKGROUND AND OBJECTIVES

Burn injuries range from minor medical issues to severe, life-threatening conditions. The severity and location of the burn dictate its treatment; while minor burns might be treatable at home, severe burns necessitate medical intervention, sometimes in specialized burn centers with extended follow-up care. This study aims to leverage artificial intelligence (AI)/machine learning (ML) to forecast potential adverse effects in burn patients.

METHODS

This retrospective analysis considered burn patients admitted to Chi Mei Medical Center from 2010 to 2019. The study employed 14 features, comprising supplementary information like prior comorbidities and laboratory results, for building models for predicting graft surgery, a prolonged hospital stay, and overall adverse effects. Overall, 70% of the data set trained the AI models, with the remaining 30% reserved for testing. Three ML algorithms of random forest, LightGBM, and logistic regression were employed with evaluation metrics of accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC).

RESULTS

In this research, out of 224 patients assessed, the random forest model yielded the highest AUC for predictions related to prolonged hospital stays (>14 days) at 81.1%, followed by the XGBoost (79.9%) and LightGBM (79.5%) models. Besides, the random forest model of the need for a skin graft showed the highest AUC (78.8%), while the random forest model and XGBoost model of the occurrence of adverse complications both demonstrated the highest AUC (87.2%) as well. Based on the best models with the highest AUC values, an AI prediction system is designed and integrated into hospital information systems to assist physicians in the decision-making process.

CONCLUSIONS

AI techniques showcased exceptional capabilities for predicting a prolonged hospital stay, the need for a skin graft, and the occurrence of overall adverse complications for burn patients. The insights from our study fuel optimism for the inception of a novel predictive model that can seamlessly meld with hospital information systems, enhancing clinical decisions and bolstering physician-patient dialogues.

Accurate and early prediction of the wound healing outcome of burn injuries using the wavelet Shannon entropy of terahertz time-domain waveforms

Significance

Severe burn injuries cause significant hypermetabolic alterations that are highly dynamic, hard to predict, and require acute and critical care. The clinical assessments of the severity of burn injuries are highly subjective and have consistently been reported to be inaccurate. Therefore, the utilization of other imaging modalities is crucial to reaching an objective and accurate burn assessment modality.

Aim

We describe a non-invasive technique using terahertz time-domain spectroscopy (THz-TDS) and the wavelet packet Shannon entropy to automatically estimate the burn depth and predict the wound healing outcome of thermal burn injuries.

Approach

We created 40 burn injuries of different severity grades in two porcine models using scald and contact methods of infliction. We used our THz portable handheld spectral reflection (PHASR) scanner to obtain the in vivo THz-TDS images. We used the energy to Shannon entropy ratio of the wavelet packet coefficients of the THz-TDS waveforms on day 0 to create supervised support vector machine (SVM) classification models. Histological assessments of the burn biopsies serve as the ground truth.

Results

We achieved an accuracy rate of 94.7% in predicting the wound healing outcome, as determined by histological measurement of the re-epithelialization rate on day 28 post-burn induction, using the THz-TDS measurements obtained on day 0. Furthermore, we report the accuracy rates of 89%, 87.1%, and 87.6% in automatic diagnosis of the superficial partial-thickness, deep partial-thickness, and full-thickness burns, respectively, using a multiclass SVM model.

Conclusions

The THz PHASR scanner promises a robust, high-speed, and accurate diagnostic modality to improve the clinical triage of burns and their management.

Artificial intelligence in the management and treatment of burns: a systematic review

BACKGROUND

Artificial intelligence (AI) is an innovative field with potential for improving burn care. This article provides an updated review on machine learning in burn care and discusses future challenges and the role of healthcare professionals in the successful implementation of AI technologies.

METHODS

A systematic search was carried out on MEDLINE, Embase and PubMed databases for English-language articles studying machine learning in burns. Articles were reviewed quantitatively and qualitatively for clinical applications, key features, algorithms, outcomes and validation methods.

RESULTS

A total of 46 observational studies were included for review. Assessment of burn depth (n = 26), support vector machines (n = 19) and 10-fold cross-validation (n = 11) were the most common application, algorithm and validation tool used, respectively.

CONCLUSION

AI should be incorporated into clinical practice as an adjunct to the experienced burns provider once direct comparative analysis to current gold standards outlining its benefits and risks have been studied. Future considerations must include the development of a burn-specific common framework. Authors should use common validation tools to allow for effective comparisons. Level I/II evidence is required to produce robust proof about clinical and economic impacts.

A Framework for Automatic Burn Image Segmentation and Burn Depth Diagnosis Using Deep Learning

Burn is a common traumatic disease with high morbidity and mortality. The treatment of burns requires accurate and reliable diagnosis of burn wounds and burn depth, which can save lives in some cases. However, due to the complexity of burn wounds, the early diagnosis of burns lacks accuracy and difference. Therefore, we use deep learning technology to automate and standardize burn diagnosis to reduce human errors and improve burn diagnosis. First, the burn dataset with detailed burn area segmentation and burn depth labelling is created. Then, an end-to-end framework based on deep learning method for advanced burn area segmentation and burn depth diagnosis is proposed. The framework is firstly used to segment the burn area in the burn images. On this basis, the calculation of the percentage of the burn area in the total body surface area (TBSA) can be realized by extending the network output structure and the labels of the burn dataset. Then, the framework is used to segment multiple burn depth areas. Finally, the network achieves the best result with IOU of 0.8467 for the segmentation of burn and no burn area. And for multiple burn depth areas segmentation, the best average IOU is 0.5144.

Towards quantitative assessment of burn based on photoacoustic and optical coherence tomography

Accurate and timely assessment of the severity of burn is essential for the treatment of burns. Currently, although most first-degree and third-degree burns are easily diagnosed through visual inspection or auxiliary diagnostic methods, the second-degree burn is still difficult to distinguish due to the ambiguity boundaries of second-degree with first-degree and third-degree burns. In this study, we proposed a non-invasive technique by combing photoacoustic imaging (PAI) and optical coherence tomography (OCT) to multi-parameter quantitatively assess the burns. The feasibility and capacity of the dual-mode PAT/OCT for assessing the burns was first testified by tissue-mimicking phantom and burn wounds in mouse pinna in vivo. The further experiments conducted on the back of rats showed that the changes in skin scattering structure, vascular morphology and blood flow provided by the dual-mode PAI/OCT system can determine distinct boundaries and depth of the burns. The experimental results prove that combined PAI/OCT as a novel method can be used to assess the severity of burn, which has the potential to diagnose the burns in clinic.

Real-time burn depth assessment using artificial networks: a large-scale, multicentre study

INTRODUCTION

Early judgment of the depth of burns is very important for the accurate formulation of treatment plans. In medical imaging the application of Artificial Intelligence has the potential for serving as a very experienced assistant to improve early clinical diagnosis. Due to lack of large volume of a particular feature, there has been almost no progress in burn field.

METHODS

484 early wound images are collected on patients who discharged home after a burn injury in 48 h, from five different levels of hospitals in Hunan Province China. According to actual healing time, all images are manually annotated by five professional burn surgeons and divided into three sets which are shallow(0-10 days), moderate(11-20 days) and deep(more than 21 days or skin graft healing). These ROIs were further divided into 5637 patches sizes 224 × 224 pixels, of which 1733 shallow, 1804 moderate, and 2100 deep. We used transfer learning suing a Pre-trained ResNet50 model and the ratio of all images is 7:1.5:1.5 for training:validation:test.

RESULTS

A novel artificial burn depth recognition model based on convolutional neural network was established and the diagnostic accuracy of the three types of burns is about 80%.

DISCUSSION

The actual healing time can be used to deduce the depth of burn involvement. The artificial burn depth recognition model can accurately infer healing time and burn depth of the patient, which is expected to be used for auxiliary diagnosis improvement.

Burn injury

Burn injuries are under-appreciated injuries that are associated with substantial morbidity and mortality. Burn injuries, particularly severe burns, are accompanied by an immune and inflammatory response, metabolic changes and distributive shock that can be challenging to manage and can lead to multiple organ failure. Of great importance is that the injury affects not only the physical health, but also the mental health and quality of life of the patient. Accordingly, patients with burn injury cannot be considered recovered when the wounds have healed; instead, burn injury leads to long-term profound alterations that must be addressed to optimize quality of life. Burn care providers are, therefore, faced with a plethora of challenges including acute and critical care management, long-term care and rehabilitation. The aim of this Primer is not only to give an overview and update about burn care, but also to raise awareness of the ongoing challenges and stigmata associated with burn injuries.

Classification of burn injury using Raman spectroscopy and optical coherence tomography: An ex-vivo study on porcine skin

Accurate depth assessment of burn wounds is a critical task to provide the right treatment and care. Currently, laser Doppler imaging is able to provide better accuracy compared to the standard clinical evaluation. However, its clinical applicability is limited by factors like scanning distance, time, and cost. Precise diagnosis of burns requires adequate structural and functional details. In this work, we evaluated the combined potential of two non-invasive optical modalities, optical coherence tomography (OCT) and Raman spectroscopy (RS), to identify degrees of burn wounds (superficial partial-thickness (SPT), deep partial-thickness (DPT), and full-thickness (FT)). OCT provides morphological information, whereas, RS provides biochemical aspects. OCT images and Raman spectra were obtained from burns created on ex-vivo porcine skin. Algorithms were developed to segment skin region and extract textural features from OCT images, and derive spectral wave features from RS. These computed features were fed into machine learning classifiers for categorization of burns. Histological results obtained from trichrome staining were used as ground-truth. The combined performance of RS-OCT reported an overall average accuracy of 85% and ROC-AUC=0.94, in distinguishing the burn wounds. The significant performance on ex vivo skin motivates to assess the feasibility of combined RS-OCT in in vivo models.

Evaluating clinical observation versus Spatial Frequency Domain Imaging (SFDI), Laser Speckle Imaging (LSI) and thermal imaging for the assessment of burn depth

While clinical examination is needed for burn severity diagnosis, several emerging technologies aim to quantify this process for added objectivity. Accurate assessments become easier after burn progression, but earlier assessments of partial thickness burn depth could lead to earlier excision and grafting and subsequent improved healing times, reduced rates of scarring/infection, and shorter hospital stays. Spatial Frequency Domain Imaging (SFDI), Laser Speckle Imaging (LSI) and thermal imaging are three non-invasive imaging modalities that have some diagnostic ability for noninvasive assessment of burn severity, but have not been compared in a controlled experiment. Here we tested the ability of these imaging techniques to assess the severity of histologically confirmed graded burns in a swine model. Controlled, graded burn wounds, 3cm in diameter were created on the dorsum of Yorkshire pigs (n=3, 45-55kg) using a custom-made burn tool that ensures consistent pressure has been employed by various burn research groups. For each pig, a total of 16 burn wounds were created on the dorsal side. Biopsies were taken for histological analysis to verify the severity of the burn. Clinical analysis, SFDI, LSI and thermal imaging were performed at 24 and 72h after burn to assess the accuracy of each imaging technique. In terms of diagnostic accuracy, using histology as a reference, SFDI (85%) and clinical analysis (83%) performed significantly better that LSI (75%) and thermography (73%) 24h after the burn. There was no statistically significant improvement from 24 to 72h across the different imaging modalities. These data indicate that these imaging modalities, and specifically SFDI, can be added to the burn clinicians' toolbox to aid in early assessment of burn severity.

Optical coherence tomography provides an optical biopsy of burn wounds in children-a pilot study

Thermic injuries are among the most severe injuries in childhood. Burn depth is the most relevant prognostic factor, and still its assessment is both difficult and controversial. This diagnostic uncertainty results in repeated wound assessments over a 10-day period and carries a relevant risk for over- and undertreatment. Precise wound assessment would thus be a significant step toward improved care. Optical coherence tomography (OCT) is a noninvasive laser-based technique with a penetration depth of ∼2  mm. It provides structural images of the skin while dynamic OCT (D-OCT) shows blood vessels. In this study, we investigated burns and scalds in 130 children with OCT and D-OCT to identify patterns of injury related to the depth of the burn wound. OCT and D-OCT images from burned skin differed consistently from normal skin. We observed several not formerly described morphologic patterns associated with burn injuries. Superficial wounds are characterized by a loss of the epidermal layer and a smooth surface. With deeper wounds, surface irregularity, loss of the dermal papillary pattern, disappearance of skin lines, and characteristic changes in the microvascular architecture were observed. This is the first systematic study of D-OCT in the assessment of burn wounds in children. A number of burn-associated patterns of injury were identified. Thus, D-OCT provided an "optical biopsy" of burn wounds that adds significant information about the severity of a burn wound.

A systematic review on the quality of measurement techniques for the assessment of burn wound depth or healing potential

PURPOSE

Reliable and valid assessment of burn wound depth or healing potential is essential to treatment decision-making, to provide a prognosis, and to compare studies evaluating different treatment modalities. The aim of this review was to critically appraise, compare and summarize the quality of relevant measurement properties of techniques that aim to assess burn wound depth or healing potential.

METHODS

A systematic literature search was performed using PubMed, EMBASE and Cochrane Library. Two reviewers independently evaluated the methodological quality of included articles using an adapted version of the Consensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist. A synthesis of evidence was performed to rate the measurement properties for each technique and to draw an overall conclusion on quality of the techniques.

RESULTS

Thirty-six articles were included, evaluating various techniques, classified as (1) laser Doppler techniques; (2) thermography or thermal imaging; (3) other measurement techniques. Strong evidence was found for adequate construct validity of laser Doppler imaging (LDI). Moderate evidence was found for adequate construct validity of thermography, videomicroscopy, and spatial frequency domain imaging (SFDI). Only two studies reported on the measurement property reliability. Furthermore, considerable variation was observed among comparator instruments.

CONCLUSIONS

Considering the evidence available, it appears that LDI is currently the most favorable technique; thereby assessing burn wound healing potential. Additional research is needed into thermography, videomicroscopy, and SFDI to evaluate their full potential. Future studies should focus on reliability and measurement error, and provide a precise description of which construct is aimed to measure.

Burn Wound Healing and Tissue Engineering

In 2016 the American Burn Association held a State of the Science conference to help identify burn research priorities for the next decade. The current paper summarizes the work of the sub-committee on Burn Wound Healing and Tissue Engineering. We first present the priorities in wound healing research over the next 10 years. We then summarize the current state of the science related to burn wound healing and tissue engineering including determination of burn depth, limiting burn injury progression, eschar removal, management of microbial contamination and wound infection, measuring wound closure, accelerating wound healing and durable wound closure, and skin substitutes and tissue engineering. Finally, a summary of the round table discussion is presented.

Quantitative long-term measurements of burns in a rat model using Spatial Frequency Domain Imaging (SFDI) and Laser Speckle Imaging (LSI)

BACKGROUND AND OJECTIVES

The current standard for diagnosis of burn severity and subsequent wound healing is through clinical examination, which is highly subjective. Several new technologies are shifting focus to burn care in an attempt to help quantify not only burn depth but also the progress of healing. While accurate early assessment of partial thickness burns is critical for dictating the course of treatment, the ability to quantitatively monitor wound status over time is critical for understanding treatment efficacy. SFDI and LSI are both non-invasive imaging modalities that have been shown to have great diagnostic value for burn severity, but have yet to be tested over the course of wound healing.

METHODS

In this study, a hairless rat model (n = 6, 300-450 g) was used with a four pronged comb to create four identical partial thickness burns (superficial n = 3 and deep n = 3) that were used to monitor wound healing over a 28 days period. Weekly biopsies were taken for histological analysis to verify wound progression. Both SFDI and LSI were performed weekly to track the evolution of hemodynamic (blood flow and oxygen saturation) and structural (reduced scattering coefficient) properties for the burns.

RESULTS

LSI showed significant changes in blood flow from baseline to 220% in superficial and 165% in deep burns by day 7. In superficial burns, blood flow returned to baseline levels by day 28, but not for deep burns where blood flow remained elevated. Smaller increases in blood flow were also observed in the surrounding tissue over the same time period. Oxygen saturation values measured with SFDI showed a progressive increase from baseline values of 66-74% in superficial burns and 72% in deep burns by day 28. Additionally, SFDI showed significant decreases in the reduced scattering coefficient shortly after the burns were created. The scattering coefficient progressively decreased in the wound area, but returned towards baseline conditions at the end of the 28 days period. Scattering changes in the surrounding tissue remained constant despite the presence of hemodynamic changes.

CONCLUSIONS

Here, we show that LSI and SFDI are capable of monitoring changes in hemodynamic and scattering properties in burn wounds over a 28 days period. These results highlight the potential insights that can be gained by using non-invasive imaging technologies to study wound healing. Further development of these technologies could be revolutionary for wound monitoring and studying the efficacy of different treatments. Lasers Surg. Med. 49:293-304, 2017. © 2017 Wiley Periodicals, Inc.

Imaging Techniques for Clinical Burn Assessment with a Focus on Multispectral Imaging

Significance: Burn assessments, including extent and severity, are some of the most critical diagnoses in burn care, and many recently developed imaging techniques may have the potential to improve the accuracy of these evaluations. Recent Advances: Optical devices, telemedicine, and high-frequency ultrasound are among the highlights in recent burn imaging advancements. We present another promising technology, multispectral imaging (MSI), which also has the potential to impact current medical practice in burn care, among a variety of other specialties. Critical Issues: At this time, it is still a matter of debate as to why there is no consensus on the use of technology to assist burn assessments in the United States. Fortunately, the availability of techniques does not appear to be a limitation. However, the selection of appropriate imaging technology to augment the provision of burn care can be difficult for clinicians to navigate. There are many technologies available, but a comprehensive review summarizing the tissue characteristics measured by each technology in light of aiding clinicians in selecting the proper device is missing. This would be especially valuable for the nonburn specialists who encounter burn injuries. Future Directions: The questions of when burn assessment devices are useful to the burn team, how the various imaging devices work, and where the various burn imaging technologies fit into the spectrum of burn care will continue to be addressed. Technologies that can image a large surface area quickly, such as thermography or laser speckle imaging, may be suitable for initial burn assessment and triage. In the setting of presurgical planning, ultrasound or optical microscopy techniques, including optical coherence tomography, may prove useful. MSI, which actually has origins in burn care, may ultimately meet a high number of requirements for burn assessment in routine clinical use.

Nanoemulsion Therapy for Burn Wounds Is Effective as a Topical Antimicrobial Against Gram-Negative and Gram-Positive Bacteria

The aim of this study is to investigate the antimicrobial efficacy of two different nanoemulsion (NE) formulations against Gram-positive and Gram-negative bacteria in an in vivo rodent scald burn model. Male Sprague-Dawley rats were anesthetized and received a partial-thickness scald burn. Eight hours after burn injury, the wound was inoculated with 1 × 10(8) colony-forming units of Pseudomonas aeruginosa or Staphylococcus aureus. Treatment groups consisted of two different NE formulations (NB-201 and NB-402), NE vehicle, or saline. Topical application of the treatment was performed at 16 and 24 hours after burn injury. Animals were killed 32 hours after burn injury, and skin samples were obtained for quantitative wound culture and determination of dermal inflammation markers. In a separate set of experiments, burn wound progression was measured histologically after 72 hours of treatment. Both NE formulations (NB-201 and NB-402) significantly reduced burn wound infections with either P. aeruginosa or S. aureus and decreased median bacterial counts at least three logs when compared with animals with saline applications (p < .0001). NB-201 and NB-402 also decreased dermal neutrophil recruitment and sequestration into the wound as measured by myeloperoxidase (MPO) assay and histopathology (p < .05). In addition, there was a decrease in the proinflammatory dermal cytokines (interleukin 1-beta [IL-1β], IL-6, and tumor necrosis factor alpha [TNF-α]) and the neutrophil chemoattractants CXCL1 and CXCL2. Using histologic examination, it was found that both NB-201 and NB-402 appeared to suppress burn wound progression 72 hours after injury. Topically applied NB-201 and NB-402 are effective in decreasing Gram-positive and Gram-negative bacteria growth in burn wounds, reducing inflammation, and abrogating burn wound progression.

Utility of spatial frequency domain imaging (SFDI) and laser speckle imaging (LSI) to non-invasively diagnose burn depth in a porcine model

Surgical intervention of second degree burns is often delayed because of the difficulty in visual diagnosis, which increases the risk of scarring and infection. Non-invasive metrics have shown promise in accurately assessing burn depth. Here, we examine the use of spatial frequency domain imaging (SFDI) and laser speckle imaging (LSI) for predicting burn depth. Contact burn wounds of increasing severity were created on the dorsum of a Yorkshire pig, and wounds were imaged with SFDI/LSI starting immediately after-burn and then daily for the next 4 days. In addition, on each day the burn wounds were biopsied for histological analysis of burn depth, defined by collagen coagulation, apoptosis, and adnexal/vascular necrosis. Histological results show that collagen coagulation progressed from day 0 to day 1, and then stabilized. Results of burn wound imaging using non-invasive techniques were able to produce metrics that correlate to different predictors of burn depth. Collagen coagulation and apoptosis correlated with SFDI scattering coefficient parameter [Formula: see text] and adnexal/vascular necrosis on the day of burn correlated with blood flow determined by LSI. Therefore, incorporation of SFDI scattering coefficient and blood flow determined by LSI may provide an algorithm for accurate assessment of the severity of burn wounds in real time.

Acute discrimination between superficial-partial and deep-partial thickness burns in a preclinical model with laser speckle imaging

A critical need exists for a robust method that enables early discrimination between superficial-partial and deep-partial thickness burn wounds. In this study, we report on the use of laser speckle imaging (LSI), a simple, non-invasive, optical imaging modality, to measure acute blood flow dynamics in a preclinical burn model. We used a heated brass comb to induce burns of varying severity to nine rats and collected raw speckle reflectance images over the course of three hours after burn. We induced a total of 12 superficial-partial and 18 deep-partial thickness burn wounds. At 3h after burn we observed a 28% and 44% decrease in measured blood flow for superficial-partial and deep-partial thickness burns, respectively, and that these reductions were significantly different (p=0.00007). This preliminary data suggests the potential role of LSI in the clinical management of burn wounds.

Combined ultrasound and photoacoustic imaging to noninvasively assess burn injury and selectively monitor a regenerative tissue-engineered construct

Current biomedical imaging tools have limitations in accurate assessment of the severity of open and deep burn wounds involving excess bleeding and severe tissue damage. Furthermore, sophisticated imaging techniques are needed for advanced therapeutic approaches such as noninvasive monitoring of stem cells seeded and applied in a biomedical 3D scaffold to enhance wound repair. This work introduces a novel application of combined ultrasound (US) and photoacoustic (PA) imaging to assess both burn injury and skin tissue regeneration. Tissue structural damage and bleeding throughout the epidermis and dermis till the subcutaneous skin layer were imaged noninvasively by US/PA imaging. Gold nanoparticle-labeled adipose-derived stem cells (ASCs) within a PEGylated fibrin 3D gel were implanted in a rat model of cutaneous burn injury. ASCs were successfully tracked till 2 weeks and were distinguished from host tissue components (e.g., epidermis, fat, and blood vessels) through spectroscopic PA imaging. The structure and function of blood vessels (vessel density and perfusion) in the wound bed undergoing skin tissue regeneration were monitored both qualitatively and semi-quantitatively by the developed imaging approach. Imaging-based analysis demonstrated ASC localization in the top layer of skin and a higher density of regenerating blood vessels in the treated groups. This was corroborated with histological analysis showing localization of fluorescently labeled ASCs and smooth muscle alpha actin-positive blood vessels. Overall, the US/PA imaging-based strategy coupled with gold nanoparticles has a great potential for stem cell therapies and tissue engineering due to its noninvasiveness, safety, selectivity, and ability to provide long-term monitoring.

Quantitative assessment of graded burn wounds in a porcine model using spatial frequency domain imaging (SFDI) and laser speckle imaging (LSI)

Accurate and timely assessment of burn wound severity is a critical component of wound management and has implications related to course of treatment. While most superficial burns and full thickness burns are easily diagnosed through visual inspection, burns that fall between these extremes are challenging to classify based on clinical appearance. Because of this, appropriate burn management may be delayed, increasing the risk of scarring and infection. Here we present an investigation that employs spatial frequency domain imaging (SFDI) and laser speckle imaging (LSI) as non-invasive technologies to characterize in-vivo burn severity. We used SFDI and LSI to investigate controlled burn wounds of graded severity in a Yorkshire pig model. Burn wounds were imaged starting at one hour after the initial injury and daily at approximately 24, 48 and 72 hours post burn. Biopsies were taken on each day in order to correlate the imaging data to the extent of burn damage as indicated via histological analysis. Changes in reduced scattering coefficient and blood flow could be used to categorize burn severity as soon as one hour after the burn injury. The results of this study suggest that SFDI and LSI information have the potential to provide useful metrics for quantifying the extent and severity of burn injuries.

Noncontact imaging of burn depth and extent in a porcine model using spatial frequency domain imaging

The standard of care for clinical assessment of burn severity and extent lacks a quantitative measurement. In this work, spatial frequency domain imaging (SFDI) was used to measure 48 thermal burns of graded severity (superficial partial, deep partial, and full thickness) in a porcine model. Functional (total hemoglobin and tissue oxygen saturation) and structural parameters (tissue scattering) derived from the SFDI measurements were monitored over 72 h for each burn type and compared to gold standard histological measurements of burn depth. Tissue oxygen saturation (stO₂) and total hemoglobin (ctHbT) differentiated superficial partial thickness burns from more severe burn types after 2 and 72 h, respectively (p < 0.01), but were unable to differentiate deep partial from full thickness wounds in the first 72 h. Tissue scattering parameters separated superficial burns from all burn types immediately after injury (p < 0.01), and separated all three burn types from each other after 24 h (p < 0.01). Tissue scattering parameters also showed a strong negative correlation to histological burn depth as measured by vimentin immunostain (r² > 0.89). These results show promise for the use of SFDI-derived tissue scattering as a correlation to burn depth and the potential to assess burn depth via a combination of SFDI functional and structural parameters.

Noncontact imaging of burn depth and extent in a porcine model using spatial frequency domain imaging

The standard of care for clinical assessment of burn severity and extent lacks a quantitative measurement. In this work, spatial frequency domain imaging (SFDI) was used to measure 48 thermal burns of graded severity (superficial partial, deep partial, and full thickness) in a porcine model. Functional (total hemoglobin and tissue oxygen saturation) and structural parameters (tissue scattering) derived from the SFDI measurements were monitored over 72 h for each burn type and compared to gold standard histological measurements of burn depth. Tissue oxygen saturation (stO₂) and total hemoglobin (ctHbT) differentiated superficial partial thickness burns from more severe burn types after 2 and 72 h, respectively (p < 0.01), but were unable to differentiate deep partial from full thickness wounds in the first 72 h. Tissue scattering parameters separated superficial burns from all burn types immediately after injury (p < 0.01), and separated all three burn types from each other after 24 h (p < 0.01). Tissue scattering parameters also showed a strong negative correlation to histological burn depth as measured by vimentin immunostain (r² > 0.89). These results show promise for the use of SFDI-derived tissue scattering as a correlation to burn depth and the potential to assess burn depth via a combination of SFDI functional and structural parameters.

Optimization of camera exposure durations for multi-exposure speckle imaging of the microcirculation

Improved Laser Speckle Contrast Imaging (LSCI) blood flow analyses that incorporate inverse models of the underlying laser-tissue interaction have been used to develop more quantitative implementations of speckle flowmetry such as Multi-Exposure Speckle Imaging (MESI). In this paper, we determine the optimal camera exposure durations required for obtaining flow information with comparable accuracy with the prevailing MESI implementation utilized in recent in vivo rodent studies. A looping leave-one-out (LOO) algorithm was used to identify exposure subsets which were analyzed for accuracy against flows obtained from analysis with the original full exposure set over 9 animals comprising n = 314 regional flow measurements. From the 15 original exposures, 6 exposures were found using the LOO process to provide comparable accuracy, defined as being no more than 10% deviant, with the original flow measurements. The optimal subset of exposures provides a basis set of camera durations for speckle flowmetry studies of the microcirculation and confers a two-fold faster acquisition rate and a 28% reduction in processing time without sacrificing accuracy. Additionally, the optimization process can be used to identify further reductions in the exposure subsets for tailoring imaging over less expansive flow distributions to enable even faster imaging.