Porcine skin possesses many of the morphological and functional features of human skin and is considered an appropriate surrogate for human skin in a wide range of preclinical animal studies. Nevertheless, there are differences in structure that may affect optical measurements of the tissue health. It is, therefore, important to understand these differences in optical properties for translation of swine data and experiments to human clinical studies. Here, we compare and contrast in-vivo measurements of the optical properties of normal and burned human and porcine skin obtained using two commercial SFDI systems.
SignificanceOver 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.AimHere, 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.ApproachTo this end, we present a comprehensive literature review of preclinical and clinical studies using ML techniques to classify the severity of burn wounds.ResultsThe 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.ConclusionsThe 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.
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