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.

Impact of hemoglobin breakdown products in the spectral analysis of burn wounds using spatial frequency domain spectroscopy.

Burn wounds and wound healing invoke several biological processes that may complicate the interpretation of spectral imaging data. Through analysis of spatial frequency domain spectroscopy data (450 to 1000 nm) obtained from longitudinal investigations using a graded porcine burn wound healing model, we have identified features in the absorption spectrum that appear to suggest the presence of hemoglobin breakdown products, e.g., methemoglobin. Our results show that the calculated concentrations of methemoglobin directly correlate with burn severity, 24 h after the injury. In addition, tissue parameters such as oxygenation (StO2) and water fraction may be underestimated by 20% and 78%, respectively, if methemoglobin is not included in the spectral analysis.

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.

Assessment of Ablative Fractional CO2 Laser and Er:YAG Laser to Treat Hypertrophic Scars in a Red Duroc Pig Model.

Hypertrophic scarring is a fibroproliferative process that occurs following a third-degree dermal burn injury, producing significant morbidity due to persistent pain, itching, cosmetic disfigurement, and loss of function due to contractures. Ablative fractional lasers have emerged clinically as a fundamental or standard therapeutic modality for hypertrophic burn scars. Yet the examination of their histopathological and biochemical mechanisms of tissue remodeling and comparison among different laser types has been lacking. In addition, deficiency of a relevant animal model limits our ability to gain a better understanding of hypertrophic scar pathophysiology. To evaluate the effect of ablative fractional lasers on hypertrophic third-degree burn scars, we have developed an in vivo Red Duroc porcine model. Third-degree burn wounds were created on the backs of animals, and burn scars were allowed to develop for 70 days before treatment. Scars received treatment with either CO2 or erbium: yttrium aluminum garnet (YAG) ablative fractional lasers. Here, we describe the effect of both lasers on hypertrophic third-degree burn scars in Red Duroc pigs. In this report, we found that Er:YAG has improved outcomes versus fractional CO2. Molecular changes noted in the areas of dermal remodeling indicated that matrix metalloproteinase 2, matrix metalloproteinase 9, and Decorin may play a role in this dermal remodeling and account for the enhanced effect of the Er:YAG laser. We have demonstrated that ablative fractional laser treatment of burn scars can lead to favorable clinical, histological, and molecular changes. This study provides support that hypertrophic third-degree burn scars can be modified by fractional laser treatment.

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.