Accurate burn wound categorization using an optimized spatial frequency domain imaging device and machine learning

Effective burn wound management is informed by accurate severity assessment. Superficial partial-thickness burns do not require surgical intervention, while deep partial-thickness and full-thickness burn wounds necessitate skin grafting to minimize infection, contraction, and hypertrophic scarring. Visual-tactile wound assessment is subjective and error-prone, especially for inexperienced practitioners. A field- and hospital-deployable device, capable of quantifying both extent and severity of burns, could enable rapid, objective burn severity measurement with commensurate improvement in patient outcomes. Our group has previously shown that spatial frequency domain imaging (SFDI), a non-invasive, wide-field optical imaging technique, can accurately assess burn wound in a porcine model of controlled, graded burn severity^{[1, 2]}. The device employed (OxImager RS) eight modulated wavelengths and five spatial frequencies and the classification of severity relies heavily on reduced scattering coefficient (tissue microstructure)^[3]. In the work that we present here, we demonstrate the burn severity prediction performance of a dramatically streamlined version of SFDI that employs a single modulated wavelength in addition to five unmodulated wavelengths. This device, known as Clarifi (Modulim, Irvine CA), is currently in refinement for ruggedization and usability for a variety of situations in which the environment is more demanding than hospital clinics. In addition, we have developed a machine learning model capable of categorizing burn severity in a porcine model of graded burns using a reduced dataset of unprocessed calibrated reflectance images generated by the device. Outputs of the model are designed to be easily interpretable and clinically actionable, exhibiting a pixelwise cross-validation accuracy of up to 99%.