Classification of burn injuries using near-infrared spectroscopy

Abstract

Early surgical management of those burn injuries that will not heal spontaneously is critical. The decision to excise and graft is based on a visual assessment that is often inaccurate but yet continues to be the primary means of grading the injury. Superficial and intermediate partial-thickness injuries generally heal with appropriate wound care while deep partial- and full-thickness injuries generally require surgery. This study explores the possibility of using near-infrared spectroscopy to provide an objective and accurate means of distinguishing shallow injuries from deeper burns that require surgery. Twenty burn injuries are studied in five animals, with burns covering <1% of the total body surface area. Carefully controlled superficial, intermediate, and deep partial-thickness injuries as well as full-thickness injuries could be studied with this model. Near-infrared reflectance spectroscopy was used to evaluate these injuries 1 to 3 hours after the insult. A probabilistic model employing partial least-squares logistic regression was used to determine the degree of injury, shallow (superficial or intermediate partial) from deep (deep partial and full thickness), based on the reflectance spectrum of the wound. A leave-animal-out cross-validation strategy was used to test the predictive ability of a 2-latent variable, partial least-squares logistic regression model to distinguish deep burn injuries from shallow injuries. The model displayed reasonable ranking quality as summarized by the area under the receiver operator characteristics curve, AUC = 0.879. Fixing the threshold for the class boundaries at 0.5 probability, the model sensitivity (true positive fraction) to separate deep from shallow burns was 0.90, while model specificity (true negative fraction) was 0.83. Using an acute porcine model of thermal burn injuries, the potential of near-infrared spectroscopy to distinguish between shallow healing burns and deeper burn injuries was demonstrated. While these results should be considered as preliminary and require clinical validation, a probabilistic model capable of differentiating these classes of burns would be a significant aid to the burn specialist.

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

“…Finally, both traditional diffuse reflectance spectroscopy (DRS) and imaging have been investigated within the context of burn wounds by various groups [16][17][18][19]. Visible and near infrared (NIR) polarized light is used to illuminate the tissue and the remitted cross-polarized light is captured with a camera or fiber to isolate subsurface features in tissue.…”

Compared application of the new OPLS-DA statistical model versus partial least squares regression to manage large numbers of variables in an injury case-control study

“…Cadieux used PLS to investigate validation of an instrument designed to conduct an Occupational Health and Safety (OHS) self-diagnosis, using workers' observations of tangible facts and actions in the workplace (Cadieux et al, 2006). PLS has also been used in injury risk assessment (Eriksson et al, 2009a;Sowa et al, 2006a), however, it has not turned into a popular method in injury epidemiology while older latent variable based methods have been used more than PLS. This may be partly due to the fact that it is not widely known by injury epidemiologists or due to its limitations.…”

Burn Depth Assessment in Thermal Skin Burns: Review

“…[45][46][47] Bu spektroskopik teknik nokta modunda kullanıldı-ğında oksijen satürasyonu ve total hemoglobin hakkında bilgi sağlar. Görüntüleme modunda ise oksijenasyondaki nisbi farklılığı ortaya koyar.…”